diff --git a/README.md b/README.md
index ab9eba7..54b960f 100644
--- a/README.md
+++ b/README.md
@@ -17,6 +17,8 @@ A player emote library for Saturn Client, with limb bending.
   `/emotes stop` and `/emotes info`. Press F5 to see yourself. It also has a Fabric client gametest
   that creates a world and screenshots each animation from the front and side, into
   `versions/<mc>/build/run/clientGameTest/screenshots/`.
+- `tools/capture/`: a Python tool that measures an emote from a store page's 3D preview and writes
+  it as Emotecraft JSON. See its README.
 
 Supported versions: 1.21.4 to 1.21.11, the same range as Saturn Client. Each version's Fabric API
 version is set in `stonecutter.properties.toml`. The code uses Mojang mappings.
diff --git a/tools/capture/.gitignore b/tools/capture/.gitignore
new file mode 100644
index 0000000..f371141
--- /dev/null
+++ b/tools/capture/.gitignore
@@ -0,0 +1,7 @@
+.venv/
+__pycache__/
+# Captures, fetched skins and scratch output: large, and regenerated by the scripts.
+frames/
+skins/
+*.log
+*.png
diff --git a/tools/capture/CheckPort.java b/tools/capture/CheckPort.java
new file mode 100644
index 0000000..43427cc
--- /dev/null
+++ b/tools/capture/CheckPort.java
@@ -0,0 +1,19 @@
+import org.saturnclient.emotes.core.bend.LimbBend;
+
+/** Prints LimbBend results for check_port.py to compare with the Python port. */
+public class CheckPort {
+    public static void main(String[] args) {
+        float[][] cases = { {-2, 10, 0.5f, 0, 0}, {-2, 10, 1.75f, 3.5f, 0}, {0, 12, 1.2f, 2.79f, 0},
+                            {0, 12, 0.7f, 3.14159f, 1}, {0, 12, -0.9f, 1.0f, 1} };
+        float[][] points = { {0, 0, 0}, {1.5f, 3, -2.25f}, {-2.25f, 10.25f, 2.25f}, {2, -2.25f, 1}, {0.5f, 12.25f, -1} };
+        float[] out = new float[3];
+        for (float[] c : cases) {
+            LimbBend b = c[4] == 1 ? LimbBend.anchoredAtEnd(c[0], c[1], c[2], c[3], 0.5f, 12)
+                                   : new LimbBend(c[0], c[1], c[2], c[3], 0.5f, 12);
+            for (float[] p : points) {
+                b.bendPoint(p[0], p[1], p[2], out);
+                System.out.printf("%f %f %f %f%n", out[0], out[1], out[2], b.angleAt(p[1]));
+            }
+        }
+    }
+}
diff --git a/tools/capture/README.md b/tools/capture/README.md
new file mode 100644
index 0000000..758abeb
--- /dev/null
+++ b/tools/capture/README.md
@@ -0,0 +1,63 @@
+# Emote capture
+
+Measures an emote from a store page's 3D preview and writes it as an Emotecraft emote our loader
+reads. It works from pixels only: it renders our own player model in the same pose from the same
+cameras and adjusts the pose until outlines and colours match. It never reads the page's animation
+data.
+
+## Setup
+
+```sh
+python3 -m venv .venv
+.venv/bin/pip install -r requirements.txt
+.venv/bin/playwright install firefox   # Playwright's own Firefox, in its cache folder
+```
+
+## Steps
+
+```sh
+.venv/bin/python capture.py <store url> <name> --skin Steve   # frames/<name>/<view>/<tick>.png + views.json
+.venv/bin/python measure.py <name> --ticks 0:13               # frames/<name>/rig.json (one loop: 13 ticks)
+.venv/bin/python fit.py <name> --symmetric --title "Twerk" --out <path>.json
+```
+
+- **`capture.py`** opens the page in headless Firefox with `viewer.js` injected, switches the
+  preview to 3D and puts a skin on it. `--skin` takes one of the dialog's presets or any Minecraft
+  username. `viewer.js` replaces the page's clock, so frames step one tick (50 ms) at a time however
+  fast the emote moves. It also finds the camera through three.js's devtools hook. Each tick is taken
+  from 10 fixed cameras with a transparent background. It also measures the loop length from the
+  frames.
+- **`skin.py`** fetches that username's skin from Mojang, so our model can wear the same texture.
+- **`measure.py`** fits our rig to every tick. It renders the model with `model.py` (a port of
+  `LimbBend` and `PoseApplier`) and `render.py`, and scores it by outline distance and colour. It
+  searches with CMA-ES on all cores. The first tick is searched in stages: torso and head, then each
+  limb from several starts, then everything, then its back-to-front twins (`flip.py`). It also finds
+  the viewer's scale then. For a whole loop, later rounds refit every tick held near the loop's
+  smoothed curve, so drift can't build up. Leaning further forward while arching further back looks
+  almost the same from every camera, so a small cost on the torso's bend picks the straightest torso.
+- **`fit.py`** smooths each channel over the loop (Fourier harmonics) and stands the emote on the
+  ground. It keys each channel wherever straight-line interpolation misses by more than 1° or 0.1 px.
+  `--symmetric` removes left/right lopsidedness (`symmetry.py`): each tick is averaged with the mirror
+  of the pose half a loop on, which keeps an even sway and drops any lean to one side.
+
+## Checking
+
+- `check_port.py` compares `model.py`'s bend maths with the Java classes (after
+  `./gradlew :core:compileJava`).
+- `synth_test.py` renders our own twerk test animation as a fake capture, so `measure.py` can be
+  scored against a known answer.
+- `symmetry.py <name>` lists the most lopsided channels.
+- `compare.py <name> <tick>` puts the captured frames next to our render of the fit.
+- `overlay.py <name>` draws both outlines on top of each other.
+- `sheet.py` tiles captured frames.
+
+To see it in game, add the JSON to the testmod's `player_animations` and `EMOTECRAFT_EMOTES`, and add
+shots to `EmotesGameTest`.
+
+## Limits
+
+- A loop takes about 45 minutes on 7 cores.
+- The emote comes out with a keyframe on almost every tick for every channel.
+- Other rigs differ from ours (a knee made of two rigid boxes, a torso that doesn't bend), so a fit is
+  our closest match, not an exact copy.
+- Extreme poses can still land in a wrong answer on the first tick. `synth_test.py`'s 80° lean does.
diff --git a/tools/capture/capture.py b/tools/capture/capture.py
new file mode 100644
index 0000000..e43ecf1
--- /dev/null
+++ b/tools/capture/capture.py
@@ -0,0 +1,140 @@
+"""Captures an emote from a store page's 3D viewer, one frame per tick from several fixed cameras.
+
+    .venv/bin/python capture.py <store url> <name> [--seconds 6]
+
+Writes frames/<name>/<view>/<tick>.png (RGBA, transparent background) and frames/<name>/views.json,
+which records each camera exactly so measure.py can render the same views.
+
+The page runs on a clock that viewer.js steps by hand, so every view of a tick shows the same moment
+and nothing is missed however fast the emote moves.
+"""
+import argparse
+import base64
+import io
+import json
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+from playwright.sync_api import sync_playwright
+
+TICK_MS = 50
+# Where the cameras look (viewer world units) and from how far. The model stands about one unit per
+# model pixel, with its middle about 12 units below the viewer's origin.
+TARGET = [0, -12, 0]
+DISTANCE = 50
+FOV = 50
+VIEWS = {f"y{yaw:03d}": (yaw, 0) for yaw in range(0, 360, 45)}
+VIEWS |= {"top000": (0, 50), "top090": (90, 50)}
+
+
+def step(page, ms, grab=False):
+    url = page.evaluate(f"window.__capture.step({ms}, {'true' if grab else 'false'})")
+    if not grab:
+        return None
+    return Image.open(io.BytesIO(base64.b64decode(url.split(",", 1)[1]))).convert("RGBA")
+
+
+def warm_up(page, frames):
+    for _ in range(frames):
+        step(page, 16)
+        page.wait_for_timeout(15)
+
+
+def set_view(page, yaw, pitch):
+    view = {"target": TARGET, "distance": DISTANCE, "yaw": yaw, "pitch": pitch, "fov": FOV}
+    page.evaluate(f"window.__capture.setView({json.dumps(view)})")
+    return view
+
+
+def choose_skin(page, skin):
+    """Puts a skin we have the texture of on the model, so measure.py can match colours too."""
+    page.evaluate("[...document.querySelectorAll('button')].find(b => b.innerText.includes(\"'s skin\")).click()")
+    warm_up(page, 40)
+    page.get_by_text("Change preview skin").last.click(force=True)
+    warm_up(page, 60)
+    preset = page.get_by_role("button", name=skin, exact=True)
+    if preset.count():
+        preset.first.click(force=True)
+    else:
+        page.get_by_placeholder("Enter a Minecraft username").fill(skin)
+        page.keyboard.press("Enter")
+    warm_up(page, 150)
+    page.keyboard.press("Escape")
+
+
+def loop_ticks(masks):
+    """The loop length in ticks: the shift that best maps the front view's masks onto themselves."""
+    best, best_err = None, None
+    for period in range(8, len(masks) // 2):
+        err = np.mean([np.mean(masks[i] != masks[i + period]) for i in range(len(masks) - period)])
+        if best_err is None or err < best_err:
+            best, best_err = period, err
+    return best, best_err
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("url")
+    parser.add_argument("name")
+    parser.add_argument("--seconds", type=float, default=6)
+    parser.add_argument("--skin", default="Steve",
+                        help="a preset from the viewer's skin dialog (Steve, Alex, ...) or a Minecraft username")
+    args = parser.parse_args()
+
+    out = Path("frames") / args.name
+    ticks = round(args.seconds * 1000 / TICK_MS)
+
+    with sync_playwright() as p:
+        browser = p.firefox.launch()
+        page = browser.new_page(viewport={"width": 1280, "height": 900})
+        page.add_init_script(path=str(Path(__file__).with_name("viewer.js")))
+        page.goto(args.url, wait_until="domcontentloaded", timeout=60000)
+        warm_up(page, 200)
+        # The toggle between the video and the 3D viewer sits in the preview's top right corner.
+        page.mouse.click(794, 177)
+        page.wait_for_selector("#skin-viewer", timeout=30000)
+        warm_up(page, 100)
+        choose_skin(page, args.skin)
+        warm_up(page, 200)
+
+        cameras = {}
+        for name, (yaw, pitch) in VIEWS.items():
+            (out / name).mkdir(parents=True, exist_ok=True)
+            cameras[name] = set_view(page, yaw, pitch)
+
+        info = page.evaluate("window.__capture.camera_info()")
+        start = page.evaluate("window.__capture.time()")
+        front_masks = []
+        for tick in range(ticks):
+            for i, (name, (yaw, pitch)) in enumerate(VIEWS.items()):
+                set_view(page, yaw, pitch)
+                # The first view advances the clock by a tick; the rest redraw the same moment.
+                frame = step(page, TICK_MS if i == 0 else 0, grab=True)
+                frame.save(out / name / f"{tick:04d}.png")
+                if name == "y000":
+                    front_masks.append(np.asarray(frame)[:, :, 3] > 127)
+            if tick % 20 == 0:
+                print(f"tick {tick}/{ticks}")
+
+        period, err = loop_ticks(front_masks)
+        meta = {
+            "url": args.url,
+            "skin": args.skin,
+            "tick_ms": TICK_MS,
+            "ticks": ticks,
+            "start_ms": start,
+            "size": list(front_masks[0].shape[::-1]),
+            "aspect": info["aspect"],
+            "near": info["near"],
+            "views": cameras,
+            "loop_ticks": period,
+            "loop_error": err,
+        }
+        (out / "views.json").write_text(json.dumps(meta, indent=2))
+        print(f"loop: {period} ticks ({period * TICK_MS / 1000:.2f} s), mismatch {err:.4f}")
+        browser.close()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tools/capture/check_port.py b/tools/capture/check_port.py
new file mode 100644
index 0000000..b421ce6
--- /dev/null
+++ b/tools/capture/check_port.py
@@ -0,0 +1,27 @@
+"""Compares model.LimbBend with the Java LimbBend, point for point.
+
+    .venv/bin/python check_port.py ../../core/build/classes/java/main   # after ./gradlew :core:compileJava
+
+Runs CheckPort.java against the compiled core classes and fails if any result differs by 1e-3 or more.
+"""
+import subprocess
+import sys
+
+import numpy as np
+
+from model import LimbBend
+
+CASES = [(-2, 10, 0.5, 0, 0), (-2, 10, 1.75, 3.5, 0), (0, 12, 1.2, 2.79, 0), (0, 12, 0.7, 3.14159, 1), (0, 12, -0.9, 1.0, 1)]
+POINTS = np.array([[0, 0, 0], [1.5, 3, -2.25], [-2.25, 10.25, 2.25], [2, -2.25, 1], [0.5, 12.25, -1]])
+
+classes = sys.argv[1]
+java = subprocess.run(["java", "-cp", classes, "CheckPort.java"], capture_output=True, text=True, check=True).stdout
+expected = np.array([[float(v) for v in line.split()] for line in java.strip().splitlines()])
+got = []
+for start, end, angle, axis, anchored in CASES:
+    b = LimbBend(start, end, angle, axis, anchored_at_end=bool(anchored))
+    bent = b.bend_points(POINTS)
+    got += [[*bent[i], b.angle_at(POINTS[i, 1])] for i in range(len(POINTS))]
+err = np.abs(np.array(got) - expected).max()
+print(f"max difference from Java: {err:.2e}")
+sys.exit(0 if err < 1e-3 else 1)
diff --git a/tools/capture/compare.py b/tools/capture/compare.py
new file mode 100644
index 0000000..78f48e7
--- /dev/null
+++ b/tools/capture/compare.py
@@ -0,0 +1,40 @@
+"""Puts captured frames next to our render of the fitted pose, with the same skin and cameras.
+
+    .venv/bin/python compare.py <name> [tick] [views]
+
+Writes frames/<name>/compare.png: for each view, theirs on the left and ours on the right.
+"""
+import json
+import sys
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+
+from model import Model, rig_to_pose
+from render import Camera, render_colors
+
+name = sys.argv[1]
+tick = int(sys.argv[2]) if len(sys.argv) > 2 else 0
+views = sys.argv[3].split(",") if len(sys.argv) > 3 else ["y000", "y090", "y180", "y270", "y045"]
+base = Path("frames") / name
+meta = json.loads((base / "views.json").read_text())
+data = json.loads((base / "rig.json").read_text())
+rig = np.array(next(t["rig"] for t in data["ticks"] if t["tick"] == tick))
+skin = np.asarray(Image.open(Path("skins") / f"{meta.get('skin', 'Steve')}.png").convert("RGBA"))
+model = Model(spacing=0.12, skin=skin)
+scale = 2
+cams = {v: Camera(meta["views"][v], meta["size"], meta["aspect"], scale) for v in views}
+ours = render_colors(np.concatenate(list(model.pose(rig_to_pose(rig)).values())), model.colors, data["calib"], cams)
+bg = np.array([40, 40, 52], np.uint8)
+tiles = []
+for v in views:
+    theirs = np.asarray(Image.open(base / v / f"{tick:04d}.png").convert("RGBA").reduce(scale))
+    a = np.where(theirs[..., 3:] > 127, theirs[..., :3], bg)
+    image, mask = ours[v]
+    b = np.where(mask[..., None], (image * 255).astype(np.uint8), bg)
+    tiles.append(np.concatenate([a, b], axis=1))
+sheet = np.concatenate(tiles, axis=0)
+ys, xs = np.nonzero((sheet != bg).any(axis=2))
+Image.fromarray(sheet[:, max(xs.min() - 8, 0):xs.max() + 8]).save(base / "compare.png")
+print("wrote", base / "compare.png")
diff --git a/tools/capture/fit.py b/tools/capture/fit.py
new file mode 100644
index 0000000..6759d09
--- /dev/null
+++ b/tools/capture/fit.py
@@ -0,0 +1,166 @@
+"""Turns measured rigs (frames/<name>/rig.json) into an Emotecraft emote our loader reads.
+
+    .venv/bin/python fit.py <name> [--out path.json] [--angle-tol 1] [--offset-tol 0.1]
+
+Every channel is keyed independently, keeping only the ticks linear interpolation can't reproduce
+within the tolerances. Values go through the inverse of EmotecraftLoader's conversions.
+"""
+import argparse
+import json
+from pathlib import Path
+
+import numpy as np
+
+from model import INDEX, Model, rest_rig, rig_to_pose
+
+PARTS = {"root": "body", "head": "head", "body": "torso", "right_arm": "rightArm", "left_arm": "leftArm",
+         "right_leg": "rightLeg", "left_leg": "leftLeg"}
+# EmotecraftLoader.REST_PIVOTS: limb positions in the file are absolute pivots.
+REST = {"right_arm": (-5, 2, 0), "left_arm": (5, 2, 0), "right_leg": (-1.9, 12, 0.1), "left_leg": (1.9, 12, 0.1)}
+CHANNELS = ["x", "y", "z", "pitch", "yaw", "roll", "bend", "axis"]
+
+
+def to_file(bone, channel, value):
+    """The inverse of EmotecraftLoader.convert, writing degrees."""
+    if channel in ("x", "y", "z"):
+        if bone == "root":
+            return value / 16 if channel == "z" else -value / 16
+        return value + REST.get(bone, (0, 0, 0))["xyz".index(channel)]
+    degrees = np.degrees(value)
+    if channel == "bend":
+        return -degrees
+    if bone == "root" and channel in ("pitch", "yaw"):
+        return -degrees
+    return degrees
+
+
+def keep(values, tol):
+    """Indices to key so linear interpolation stays within tol of every sample (Douglas-Peucker)."""
+    keys = {0, len(values) - 1}
+
+    def split(a, b):
+        if b - a < 2:
+            return
+        t = np.arange(a, b + 1)
+        line = values[a] + (values[b] - values[a]) * (t - a) / (b - a)
+        err = np.abs(values[a:b + 1] - line)
+        i = int(np.argmax(err))
+        if err[i] > tol:
+            keys.add(a + i)
+            split(a, a + i)
+            split(a + i, b)
+
+    split(0, len(values) - 1)
+    return sorted(keys)
+
+
+def ground(rigs):
+    """Shifts every tick vertically so the lowest foot over the whole emote is where a standing
+    player's feet are. The viewer offset was a guess (measure.py keeps it fixed), so the measured
+    height is only right up to a constant."""
+    model = Model()
+    lowest = lambda rig: max(model.pose(rig_to_pose(rig))[leg][:, 1].max() for leg in ("right_leg", "left_leg"))
+    shift = lowest(rest_rig()) - max(lowest(r) for r in rigs)  # model Y points down
+    for r in rigs:
+        r[INDEX["root_y"]] += shift
+    return shift
+
+
+def periodic_smooth(values, harmonics, angle):
+    """A loop's channel kept to its first few Fourier harmonics: removes the fit's tick-to-tick noise
+    and joins the loop's seam. Returns the smoothed samples plus the loop's next sample (its start
+    again, a whole number of turns on for an angle that spins)."""
+    n = len(values)
+    turns = 0.0
+    if angle:
+        # How far the angle travels in one loop: nothing for a sway, whole turns for a spin.
+        step = (values[0] - values[-1] + np.pi) % (2 * np.pi) - np.pi
+        turns = values[-1] + step - values[0]
+    ramp = turns * np.arange(n + 1) / n
+    spectrum = np.fft.rfft(values - ramp[:n])
+    spectrum[harmonics + 1:] = 0
+    smooth = np.fft.irfft(spectrum, n)
+    return np.append(smooth, smooth[0]) + ramp
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("name")
+    parser.add_argument("--out")
+    parser.add_argument("--angle-tol", type=float, default=1.0, help="degrees")
+    parser.add_argument("--offset-tol", type=float, default=0.1, help="pixels")
+    parser.add_argument("--no-ground", action="store_true", help="keep the measured height")
+    parser.add_argument("--harmonics", type=int, default=4,
+                        help="for a whole loop, how many Fourier harmonics each channel keeps (0 = no smoothing)")
+    parser.add_argument("--rig", help="read this rig file instead of frames/<name>/rig.json")
+    parser.add_argument("--symmetric", action="store_true",
+                        help="for a whole loop that sways once per loop, remove its left/right lopsidedness "
+                             "(see symmetry.py)")
+    parser.add_argument("--title", help="the emote's name (default: from <name>)")
+    parser.add_argument("--description", help="default: where it was measured from")
+    parser.add_argument("--author", default="Saturn capture")
+    args = parser.parse_args()
+
+    base = Path("frames") / args.name
+    data = json.loads(Path(args.rig).read_text() if args.rig else (base / "rig.json").read_text())
+    meta = json.loads((base / "views.json").read_text())
+    ticks = data["ticks"]
+    loop = meta.get("loop_ticks")
+    rigs = [np.array(t["rig"]) for t in ticks]
+    if args.symmetric:
+        if not (loop and len(rigs) == loop):
+            parser.error("--symmetric needs a whole loop")
+        from symmetry import symmetrize
+        rigs = list(symmetrize(rigs, sway=True))
+        print("made symmetric: each tick averaged with the mirror of the pose half a loop on")
+    if not args.no_ground:
+        print(f"grounded: moved {ground(rigs):+.2f} px down")
+    poses = [rig_to_pose(r) for r in rigs]
+    first = ticks[0]["tick"]
+    whole_loop = bool(loop) and len(poses) == loop
+    if whole_loop and args.harmonics:
+        print(f"smoothed each channel to {args.harmonics} harmonics of the {loop}-tick loop")
+
+    moves = {}
+    total = 0
+    for bone, part in PARTS.items():
+        for channel in CHANNELS:
+            raw = np.array([getattr(p[bone], channel) for p in poses], float)
+            angle = channel not in ("x", "y", "z")
+            if angle:
+                raw = np.unwrap(raw)
+            if whole_loop:
+                # A loop's last tick blends back into its first.
+                raw = periodic_smooth(raw, args.harmonics, angle) if args.harmonics else np.append(raw, raw[0])
+            values = np.array([to_file(bone, channel, v) for v in raw])
+            if np.all(np.abs(values - values[0]) < 1e-6) and abs(values[0]) < 1e-6 and channel != "x":
+                continue
+            tol = args.offset_tol if channel in ("x", "y", "z") else args.angle_tol
+            if bone == "root" and channel in ("x", "y", "z"):
+                tol /= 16
+            for i in keep(values, tol):
+                moves.setdefault(i, {}).setdefault(part, {})[channel] = round(float(values[i]), 4)
+                total += 1
+
+    emote = {
+        "version": 3,
+        "name": args.title or args.name.replace("_", " ").title(),
+        "description": args.description or f"Measured from {meta['url']}",
+        "author": args.author,
+        "emote": {
+            "degrees": True,
+            "isLoop": bool(loop),
+            "returnTick": 0,
+            "endTick": len(poses) - 1,
+            "stopTick": len(poses) if whole_loop else len(poses) - 1,
+            "moves": [{"tick": i, "easing": "LINEAR", **parts} for i, parts in sorted(moves.items())],
+        },
+    }
+    out = Path(args.out) if args.out else base / f"{args.name}.json"
+    out.write_text(json.dumps(emote, indent=2))
+    print(f"wrote {out}: {len(emote['emote']['moves'])} ticks keyed, {total} channel keyframes "
+          f"(from {len(poses)} samples, starting at captured tick {first})")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tools/capture/flip.py b/tools/capture/flip.py
new file mode 100644
index 0000000..248362b
--- /dev/null
+++ b/tools/capture/flip.py
@@ -0,0 +1,55 @@
+"""The back-to-front twin of a rig, which covers the same space with the textures turned round.
+
+The head and torso boxes are centred on their pivots, so spinning either half a turn around its own
+vertical axis leaves it covering the same space; only which face shows changes. The arms hang from
+the torso's sides, so spinning the torso carries each arm to the other side. That twin (torso and
+head facing backwards, arms swapped) is a separate local minimum the search can't walk out of, so
+measure.py jumps to it directly, refines it, and keeps whichever scores lower. Only the colours tell
+them apart.
+
+In rig terms: the torso and head get R Ry(pi); each arm takes the other's rotation times Ry(pi) (a
+half turn around its own length, which moves its off-centre box to the other side of its pivot); and
+bends keep their shape with their direction turned half round, since the frame they're measured in
+turned. The legs hang from the hips and don't change.
+"""
+import numpy as np
+
+from model import INDEX, rotation_zyx
+
+SPIN = np.diag([-1.0, 1.0, -1.0])  # Ry(pi)
+ARMS = {"right_arm": "left_arm", "left_arm": "right_arm"}
+
+
+def euler_zyx(r):
+    """(pitch, yaw, roll) with rotation_zyx(pitch, yaw, roll) == r."""
+    yaw = np.arcsin(np.clip(-r[2, 0], -1, 1))
+    return np.arctan2(r[2, 1], r[2, 2]), yaw, np.arctan2(r[1, 0], r[0, 0])
+
+
+def _spun(rig, source):
+    g = lambda c: rig[INDEX[f"{source}_{c}"]]
+    return euler_zyx(rotation_zyx(g("pitch"), g("yaw"), g("roll")) @ SPIN)
+
+
+def flip_head(rig):
+    """Just the head spun round: a cube on its pivot, so the same space facing the other way."""
+    out = rig.copy()
+    for c, v in zip(("pitch", "yaw", "roll"), _spun(rig, "head")):
+        out[INDEX[f"head_{c}"]] = v
+    return out
+
+
+def twins(rig):
+    """Every back-to-front twin worth trying: the torso with its arms, the head, and both."""
+    return [flip(rig), flip_head(rig), flip_head(flip(rig))]
+
+
+def flip(rig):
+    out = rig.copy()
+    for part, source in [("body", "body"), ("head", "head"), *ARMS.items()]:
+        for c, v in zip(("pitch", "yaw", "roll"), _spun(rig, source)):
+            out[INDEX[f"{part}_{c}"]] = v
+    for part, source in [("body", "body"), *ARMS.items()]:
+        out[INDEX[f"{part}_bend"]] = rig[INDEX[f"{source}_bend"]]
+        out[INDEX[f"{part}_axis"]] = rig[INDEX[f"{source}_axis"]] + np.pi
+    return out
diff --git a/tools/capture/measure.py b/tools/capture/measure.py
new file mode 100644
index 0000000..b5f0716
--- /dev/null
+++ b/tools/capture/measure.py
@@ -0,0 +1,341 @@
+"""Fits our rig to captured frames, tick by tick, by rendering it and matching outlines and colours.
+
+    .venv/bin/python measure.py <name> [--ticks 0:13] [--workers 7]
+
+Writes frames/<name>/rig.json: the viewer calibration and one rig vector per tick (see model.RIG).
+
+The first tick has nothing to start from, so it's searched in stages: the torso and head, then each
+limb from several starts, then everything together, then its back-to-front twins (see flip.py). It
+also finds the viewer's scale. Later ticks start from the tick before.
+
+For a whole loop, that first pass is only a starting point: warm starts carry any drift from tick to
+tick. Each later round smooths every channel over the loop (a few Fourier harmonics, which have to
+close), and refits every tick held near that curve instead of near its neighbour.
+"""
+import argparse
+import json
+import multiprocessing
+import time
+from pathlib import Path
+
+import cma
+import numpy as np
+from PIL import Image
+
+from flip import twins
+from model import INDEX, RIG, Model, rest_rig, rig_to_pose
+from render import Capture, score
+
+# How far CMA-ES first looks along each parameter: pixels for offsets, radians for angles.
+STEP = np.array([2.0 if name.endswith(("_x", "_y", "_z")) else 0.35 for name in RIG])
+CALIB = ["scale", "view_x", "view_y", "view_z"]
+CALIB_STEP = np.array([0.05, 2, 2, 2])
+# Only the scale is fitted. Moving the viewer, the whole player and the hips all slide the whole model,
+# so no frame can tell them apart: the viewer's offset stays fixed (it centres the player on its
+# origin), and fit.py stands the finished emote on the ground instead.
+CALIB_FREE = [0]
+# Channels a silhouette barely sees (a limb's twist, which way a straight limb would bend) drift
+# unless something holds them near zero.
+QUIET = np.array([1.0 if name.endswith(("_yaw", "_axis")) and not name.startswith(("root", "body")) else 0.0
+                  for name in RIG])
+QUIET_WEIGHT = 0.02
+SMOOTH_WEIGHT = 0.05
+# The search scores colour lightly in a few views: outlines give it a smooth slope to follow, and
+# colour changes abruptly as parts slide across texture edges, which strands it. Choosing between a
+# fit and its back-to-front twins (whose outlines match) is colour's job, so that comparison weighs
+# colour heavily in every view.
+COLOR_VIEWS = {"y000", "y090", "y180", "y270"}
+JUDGE_COLOR_WEIGHT = 2.0
+
+# Anatomy, as soft limits: in a tight pose like a crouch, many limb twists cast nearly the same outline,
+# and these pick the natural one. Nothing is penalised inside the ranges.
+FOLD = {"right_leg": np.pi, "left_leg": np.pi, "right_arm": 0.0, "left_arm": 0.0}  # knees back, elbows forward
+FOLD_RANGE = np.radians(70)
+ROLL_RANGE = np.radians(60)
+PRIOR_WEIGHT = 0.5
+HIP_CENTRE_WEIGHT = 0.002
+# Leaning the torso further forward while arching it further back looks nearly the same from every
+# camera, so a small cost on the torso's bend picks the straightest torso that fits.
+BODY_BEND_WEIGHT = 0.008
+# Where each bend direction is kept (see canonical): half a turn either side of this. Knees fold back
+# and elbows forward; a torso may hunch (0) or arch (pi), so both sit well inside its range.
+AXIS_CENTRE = FOLD | {"body": np.pi / 2}
+
+
+def wrap(a):
+    return (a + np.pi) % (2 * np.pi) - np.pi
+
+
+ANGLES = np.array([not name.endswith(("_x", "_y", "_z")) for name in RIG])
+
+
+def change(rig, previous):
+    """How far each channel moved, the short way round for angles: canonical() keeps them within
+    -pi..pi, so a knee folding back straddles the edge and would look like it swung a whole turn."""
+    d = rig - previous
+    return np.where(ANGLES, wrap(d), d)
+
+
+def anatomy(rig):
+    """How far outside natural joint ranges the rig is (0 inside them)."""
+    total = 0.0
+    for limb, fold in FOLD.items():
+        bend = rig[INDEX[f"{limb}_bend"]]
+        # A negative bend folds the other way.
+        axis = rig[INDEX[f"{limb}_axis"]] + (np.pi if bend < 0 else 0)
+        # Which way a limb folds only matters as much as it bends.
+        total += np.sin(min(abs(bend), np.pi) / 2) * max(0.0, abs(wrap(axis - fold)) - FOLD_RANGE) ** 2
+        total += max(0.0, abs(wrap(rig[INDEX[f"{limb}_roll"]])) - ROLL_RANGE) ** 2
+    return PRIOR_WEIGHT * total + HIP_CENTRE_WEIGHT * rig[INDEX["hip_x"]] ** 2
+
+# The hips carry everything, so the whole player's offset would only duplicate them; it stays at zero.
+# Turning the whole player duplicates turning every part, so it's only fitted with --root, for flips
+# and spins, where it keeps the keyframes simple.
+FITTED = [i for i, n in enumerate(RIG) if not n.startswith("root")]
+ROOT_TURN = [INDEX[n] for n in ("root_pitch", "root_yaw", "root_roll")]
+LIMBS = ("right_leg", "left_leg", "right_arm", "left_arm")
+ALL_PARTS = ("head", "body", "right_arm", "left_arm", "right_leg", "left_leg")
+
+# Per-process state, set by init_worker, so the pool only receives parameter vectors.
+_state = {}
+
+
+def init_worker(directory, skin_path, tick):
+    skin = np.asarray(Image.open(skin_path).convert("RGBA"))
+    cap = Capture(directory)
+    model = Model(skin=skin)
+    # Where each part's points sit in model.colors, to score some of the parts on their own.
+    bounds = np.cumsum([0] + [len(model.local[p]) for p in model.local])
+    spans = {p: (bounds[i], bounds[i + 1]) for i, p in enumerate(model.local)}
+    _state.update(model=model, spans=spans, cameras=cap.cameras, targets=cap.targets(tick))
+
+
+def loss(job):
+    """job = (full vector: rig + calib, previous rig or None, parts to score[, judging]). Scoring only
+    some parts (the torso stage) counts just their points outside the captured outline. Judging weighs
+    colour heavily in every view, to pick between twins."""
+    v, previous, parts, *judging = job
+    judging = bool(judging and judging[0])
+    rig, calib = v[:len(RIG)], v[len(RIG):]
+    m = _state["model"]
+    posed = m.pose(rig_to_pose(rig))
+    parts = parts or ALL_PARTS
+    pts = np.concatenate([posed[p] for p in parts])
+    colors = np.concatenate([m.colors[slice(*_state["spans"][p])] for p in parts])
+    total = score(pts, calib, _state["cameras"], _state["targets"], colors=colors,
+                  color_views=None if judging else COLOR_VIEWS, one_way=len(parts) < len(ALL_PARTS),
+                  color_weight=JUDGE_COLOR_WEIGHT if judging else None)
+    total += QUIET_WEIGHT * np.sum(QUIET * np.sin(rig) ** 2)
+    total += anatomy(rig)
+    total += BODY_BEND_WEIGHT * rig[INDEX["body_bend"]] ** 2
+    if previous is not None:
+        total += SMOOTH_WEIGHT * np.sum((change(rig, previous) / STEP) ** 2) / len(rig)
+    return total
+
+
+def fit(pool, x0, free, sigma, evals, previous=None, parts=None, popsize=24, seed=1):
+    """CMA-ES over the indices in free (of rig + calib); the rest stay at x0."""
+    x0 = np.asarray(x0, float)
+    steps = np.concatenate([STEP, CALIB_STEP])[free]
+
+    def expand(sub):
+        v = x0.copy()
+        v[free] = sub
+        return v
+
+    es = cma.CMAEvolutionStrategy(x0[free], sigma, {
+        "CMA_stds": steps, "popsize": popsize, "maxfevals": evals, "verbose": -9, "seed": seed, "tolfun": 1e-4,
+    })
+    while not es.stop():
+        subs = es.ask()
+        es.tell(subs, pool.map(loss, [(expand(s), previous, parts) for s in subs]))
+    return expand(es.result.xbest), es.result.fbest
+
+
+def canonical(v):
+    """A negative bend is the same shape as a positive one folding the other way. Bend directions are
+    kept within half a turn of AXIS_CENTRE, so a knee folding back doesn't sit on the edge of the range
+    and jump a whole turn between ticks."""
+    v = v.copy()
+    for part, centre in AXIS_CENTRE.items():
+        b, a = INDEX[f"{part}_bend"], INDEX[f"{part}_axis"]
+        if v[b] < 0:
+            v[b], v[a] = -v[b], v[a] + np.pi
+        v[a] = wrap(v[a] - centre) + centre
+    return v
+
+
+def loop_curve(rigs, harmonics):
+    """Each channel of a loop's rigs kept to its first few Fourier harmonics: the loop's own smooth
+    path, which has to come back to where it started."""
+    from fit import periodic_smooth
+    rigs = np.array(rigs)
+    curve = np.empty_like(rigs)
+    for i in range(rigs.shape[1]):
+        values = np.unwrap(rigs[:, i]) if ANGLES[i] else rigs[:, i]
+        curve[:, i] = periodic_smooth(values, harmonics, bool(ANGLES[i]))[:-1]
+    return curve
+
+
+# Starting points for each limb on its own: swung back, hanging, or forward, straight or folded the
+# natural way. One start lands in a different local minimum every run; the best of these doesn't.
+LIMB_STARTS = [(pitch, bend) for pitch in (-1.2, 0.0, 1.2) for bend in (0.0, 1.5)]
+
+
+def fit_limbs(pool, x, fitted, starts, evals):
+    """Fits each limb in turn with the others held, keeping the best of the starts."""
+    fx = None
+    for limb in LIMBS:
+        free = [i for i in fitted if RIG[i].startswith(limb)]
+        best = None
+        for pitch, bend in starts or [(None, None)]:
+            x0 = x.copy()
+            if pitch is not None:
+                x0[free] = 0
+                x0[INDEX[f"{limb}_pitch"]] = pitch
+                x0[INDEX[f"{limb}_bend"]] = bend
+                x0[INDEX[f"{limb}_axis"]] = FOLD[limb]
+            result = fit(pool, x0, free, 1.0 if starts else 0.4, evals)
+            if best is None or result[1] < best[1]:
+                best = result
+        x, fx = best
+    return x, fx
+
+
+def refine_scale(pool, x):
+    """The viewer scale on its own, once the pose explains the whole outline."""
+    from scipy.optimize import minimize_scalar
+
+    def f(scale):
+        v = x.copy()
+        v[len(RIG)] = scale
+        return pool.map(loss, [(v, None, None)])[0]
+
+    result = minimize_scalar(f, bounds=(x[len(RIG)] * 0.85, x[len(RIG)] * 1.15), method="bounded",
+                             options={"xatol": 1e-3})
+    v = x.copy()
+    v[len(RIG)] = result.x
+    return v
+
+
+def settle_facing(pool, x, fx, fitted, previous, log):
+    """Tries each back-to-front twin of a fit, refined, and keeps the best. They cover nearly the same
+    outline, so the search can't get from one to another by itself; only colour decides."""
+    judge = lambda v: pool.map(loss, [(v, previous, None, True)])[0]
+    best, best_judged = (x, fx), judge(x)
+    for twin in twins(x[:len(RIG)]):
+        candidate = fit(pool, np.concatenate([twin, x[len(RIG):]]), fitted, 0.3, 1500, previous)
+        judged = judge(candidate[0])
+        if judged < best_judged:
+            log(f"  a turned-round twin fits better: {judged:.3f} < {best_judged:.3f} (judged on colour)")
+            best, best_judged = candidate, judged
+    return best
+
+
+def first_tick(pool, calib, fitted, log):
+    """Tries the player facing both ways, all the way through, since no single stage can tell them
+    apart reliably: the torso, then each limb from several starts, then each limb again with the
+    others in place, then everything together. The scale is held until the pose explains the whole
+    outline (shrinking always helps a partly wrong pose), then fitted on its own."""
+    torso = [i for i in fitted if RIG[i].startswith(("root", "hip", "body", "head"))]
+    x = np.concatenate([rest_rig(), calib])
+    # The torso and head alone, kept inside the outline and matched by colour.
+    x, fx = fit(pool, x, torso, 1.0, 3000, parts=("head", "body"))
+    log(f"  torso {fx:.3f}")
+    x, fx = fit_limbs(pool, x, fitted, LIMB_STARTS, 500)
+    log(f"  limbs {fx:.3f}")
+    x, fx = fit_limbs(pool, x, fitted, None, 1000)
+    x, fx = fit(pool, x, fitted, 0.3, 3000)
+    log(f"  together {fx:.3f}")
+    # Facing can be wrong at any stage; settle it once the pose explains the whole outline.
+    x, fx = settle_facing(pool, x, fx, fitted, None, log)
+    x, fx = fit(pool, x, fitted, 0.3, 6000)
+    x = refine_scale(pool, x)
+    return fit(pool, x, fitted, 0.2, 3000)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("name")
+    parser.add_argument("--ticks", default="0:13")
+    parser.add_argument("--calib", default="0.9375,0,-1.5,0",
+                        help="viewer scale (a starting guess) and offset (kept) as scale,x,y,z")
+    parser.add_argument("--workers", type=int, default=max(1, multiprocessing.cpu_count() - 1))
+    parser.add_argument("--rounds", type=int, default=2,
+                        help="for a whole loop, how many times to refit every tick held near the loop's smoothed curve")
+    parser.add_argument("--harmonics", type=int, default=2,
+                        help="Fourier harmonics in that curve: few, since it only decides what the frames can't")
+    parser.add_argument("--root", action="store_true", help="also turn the whole player (flips and spins)")
+    parser.add_argument("--resume", action="store_true",
+                        help="start from the existing rig.json and only run the rounds")
+    args = parser.parse_args()
+
+    directory = Path("frames") / args.name
+    meta = json.loads((directory / "views.json").read_text())
+    skin_path = Path("skins") / f"{meta.get('skin', 'Steve')}.png"
+    if not skin_path.exists():
+        from skin import fetch
+        skin_path.write_bytes(fetch(meta["skin"])[0])
+    calib = [float(c) for c in args.calib.split(",")]
+    fitted = sorted(FITTED + (ROOT_TURN if args.root else []))
+    start, end = map(int, args.ticks.split(":"))
+
+    ticks = list(range(start, end))
+    whole_loop = meta.get("loop_ticks") == len(ticks)
+    out = directory / "rig.json"
+    by_tick = {}
+
+    def save():
+        results = [{k: v for k, v in by_tick[t].items() if k != "rig_array"} for t in ticks if t in by_tick]
+        out.write_text(json.dumps({"rig": RIG, "calib": calib, "scale": 4, "ticks": results}, indent=2))
+
+    def record(tick, x, fx, label, t0):
+        rig = canonical(x[:len(RIG)])
+        init_worker(directory, skin_path, tick)
+        per_view = score(np.concatenate(list(_state["model"].pose(rig_to_pose(rig)).values())), calib,
+                         _state["cameras"], _state["targets"], True, _state["model"].colors, COLOR_VIEWS)
+        print(f"tick {tick}{label}: score {fx:.3f}, worst view {max(per_view, key=per_view.get)} "
+              f"{max(per_view.values()):.2f}, {time.time() - t0:.0f}s", flush=True)
+        by_tick[tick] = {"tick": tick, "rig": rig.tolist(), "rig_array": rig, "score": fx, "views": per_view}
+        save()
+
+    log = lambda m: print(m, flush=True)
+    if args.resume:
+        saved = json.loads(out.read_text())
+        calib = saved["calib"]
+        for t in saved["ticks"]:
+            by_tick[t["tick"]] = t | {"rig_array": canonical(np.array(t["rig"]))}
+    else:
+        # First pass: each tick warm-started from the one before.
+        previous = None
+        for tick in ticks:
+            t0 = time.time()
+            with multiprocessing.Pool(args.workers, init_worker, (directory, skin_path, tick)) as pool:
+                if previous is None:
+                    x, fx = first_tick(pool, calib, fitted, log)
+                    calib = [float(c) for c in x[len(RIG):]]
+                    log("calibration: " + ", ".join(f"{name} {c:.3f}" for name, c in zip(CALIB, calib)))
+                else:
+                    x, fx = fit(pool, np.concatenate([previous, calib]), fitted, 0.3, 4000, previous)
+                    x, fx = settle_facing(pool, x, fx, fitted, previous, log)
+            record(tick, x, fx, "", t0)
+            previous = by_tick[tick]["rig_array"]
+
+    # Later rounds, for a loop: every tick held near the loop's smoothed curve. No tick depends on the
+    # one fitted before it, so drift can't build up, and the curve has to close.
+    for r in range(args.rounds if whole_loop else 0):
+        curve = loop_curve([by_tick[t]["rig_array"] for t in ticks], args.harmonics)
+        for i, tick in enumerate(ticks):
+            t0 = time.time()
+            anchor = canonical(curve[i])
+            with multiprocessing.Pool(args.workers, init_worker, (directory, skin_path, tick)) as pool:
+                # From its own fit and from the curve: the curve can pull a tick off a ridge its own
+                # fit slid along.
+                x, fx = min((fit(pool, np.concatenate([start, calib]), fitted, 0.2, 2000, anchor)
+                             for start in (by_tick[tick]["rig_array"], anchor)), key=lambda c: c[1])
+            record(tick, x, fx, f" (round {r + 1})", t0)
+    print("wrote", directory / "rig.json")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tools/capture/model.py b/tools/capture/model.py
new file mode 100644
index 0000000..77c7711
--- /dev/null
+++ b/tools/capture/model.py
@@ -0,0 +1,351 @@
+"""The player model and our pose maths, ported from the library so poses can be rendered in Python.
+
+Mirrors core's LimbBend and Bezier and the fabric mod's PoseApplier: same part boxes and pivots, same
+rotation order (ModelPart's Z, then Y, then X), the same bends, and the same carrying of the head and
+arms by a bent torso. check_port.py compares it against the Java classes.
+
+Model space is Minecraft's: pixels, Y down, the player faces -Z, and its right side is at -X.
+"""
+from dataclasses import dataclass
+
+import numpy as np
+
+BONES = ["root", "head", "body", "right_arm", "left_arm", "right_leg", "left_leg"]
+
+# Default pivots and boxes (from, size) of the vanilla player model with wide arms.
+PIVOTS = {
+    "head": (0, 0, 0),
+    "body": (0, 0, 0),
+    "right_arm": (-5, 2, 0),
+    "left_arm": (5, 2, 0),
+    "right_leg": (-1.9, 12, 0),
+    "left_leg": (1.9, 12, 0),
+}
+BOXES = {
+    "head": ((-4, -8, -4), (8, 8, 8)),
+    "body": ((-4, 0, -2), (8, 12, 4)),
+    "right_arm": ((-3, -2, -2), (4, 12, 4)),
+    "left_arm": ((-1, -2, -2), (4, 12, 4)),
+    "right_leg": ((-2, 0, -2), (4, 12, 4)),
+    "left_leg": ((-2, 0, -2), (4, 12, 4)),
+}
+# The outer skin layer (hat, jacket, sleeves, pants) is this much bigger on every side.
+OVERLAY = {"head": 0.5, "body": 0.25, "right_arm": 0.25, "left_arm": 0.25, "right_leg": 0.25, "left_leg": 0.25}
+# Where each part's base and outer layer start in a 64x64 skin.
+UV = {
+    "head": ((0, 0), (32, 0)),
+    "body": ((16, 16), (16, 32)),
+    "right_arm": ((40, 16), (40, 32)),
+    "left_arm": ((32, 48), (48, 48)),
+    "right_leg": ((0, 16), (0, 32)),
+    "left_leg": ((16, 48), (0, 48)),
+}
+
+SHARPNESS = 0.5
+SEGMENTS = 12
+ARC_SAMPLES = 64
+ROOT_PIVOT_Y = 12.8
+
+
+@dataclass
+class Transform:
+    """BoneTransform: offset in pixels, rotations and bend in radians."""
+    x: float = 0
+    y: float = 0
+    z: float = 0
+    pitch: float = 0
+    yaw: float = 0
+    roll: float = 0
+    bend: float = 0
+    axis: float = 0
+
+
+def rotation_zyx(pitch, yaw, roll):
+    """The matrix ModelPart.translateAndRotate applies: Rz(roll) Ry(yaw) Rx(pitch)."""
+    cx, sx = np.cos(pitch), np.sin(pitch)
+    cy, sy = np.cos(yaw), np.sin(yaw)
+    cz, sz = np.cos(roll), np.sin(roll)
+    rx = np.array([[1, 0, 0], [0, cx, -sx], [0, sx, cx]])
+    ry = np.array([[cy, 0, sy], [0, 1, 0], [-sy, 0, cy]])
+    rz = np.array([[cz, -sz, 0], [sz, cz, 0], [0, 0, 1]])
+    return rz @ ry @ rx
+
+
+def axis_angle(axis, angle):
+    x, y, z = axis
+    c, s = np.cos(angle), np.sin(angle)
+    k = np.array([[0, -z, y], [z, 0, -x], [-y, x, 0]])
+    return np.eye(3) + s * k + (1 - c) * (k @ k)
+
+
+def _bezier(points, t):
+    """Points on a cubic Bézier at each t (an array), like core's Bezier.point."""
+    t = np.asarray(t, float)[:, None]
+    p0, p1, p2, p3 = points
+    u = 1 - t
+    return u ** 3 * p0 + 3 * u * u * t * p1 + 3 * u * t * t * p2 + t ** 3 * p3
+
+
+def _bezier_derivative(points, t):
+    t = np.asarray(t, float)[:, None]
+    p0, p1, p2, p3 = points
+    u = 1 - t
+    return 3 * u * u * (p1 - p0) + 6 * u * t * (p2 - p1) + 3 * t * t * (p3 - p2)
+
+
+class LimbBend:
+    """Port of core's LimbBend. Vectorised over points."""
+
+    def __init__(self, start, end, angle, axis, sharpness=SHARPNESS, segments=SEGMENTS, anchored_at_end=False):
+        self.start = start
+        self.length = end - start
+        self.anchored_at_end = anchored_at_end
+        self.segments = segments
+        self.angle = angle
+        self.dir = np.array([np.sin(axis), 0, -np.cos(axis)])
+        self.n = np.array([self.dir[2], 0, -self.dir[0]])
+        self.ring = np.zeros((segments + 1, 3))
+        self.ring_angle = np.zeros(segments + 1)
+        if self.straight:
+            self.ring[:, 1] = start + self.length * np.arange(segments + 1) / segments
+            return
+        self._build(self._spine(min(1, max(0, sharpness))))
+
+    @property
+    def straight(self):
+        return abs(self.angle) < 1e-4
+
+    def _spine(self, sharpness):
+        half = self.length / 2
+        top = np.array([0, self.start, 0.0])
+        joint = np.array([0, self.start + half, 0.0])
+        lower = np.array([self.dir[0] * np.sin(self.angle), np.cos(self.angle), self.dir[2] * np.sin(self.angle)])
+        bottom = joint + lower * half
+        pull = 2 / 3 + sharpness / 3
+        return [top, top + (joint - top) * pull, bottom + (joint - bottom) * pull, bottom]
+
+    def _build(self, curve):
+        ts = np.arange(ARC_SAMPLES + 1) / ARC_SAMPLES
+        pts = _bezier(curve, ts)
+        lengths = np.concatenate([[0], np.cumsum(np.linalg.norm(np.diff(pts, axis=0), axis=1))])
+        total = lengths[-1]
+        scale = self.length / total
+        top = pts[0]
+        # For each ring, the arc-length sample it falls in (LimbBend's while loop), then t within it.
+        target = total * np.arange(self.segments + 1) / self.segments
+        sample = np.clip(np.searchsorted(lengths, target, side="left") - 1, 0, ARC_SAMPLES - 1)
+        span = lengths[sample + 1] - lengths[sample]
+        f = np.where(span > 0, (target - lengths[sample]) / np.where(span > 0, span, 1), 0)
+        t = ts[sample] + (ts[sample + 1] - ts[sample]) * f
+        self.ring = top + (_bezier(curve, t) - top) * scale
+        tangent = _bezier_derivative(curve, t)
+        self.ring_angle = np.arctan2(tangent @ self.dir, tangent[:, 1])
+
+    def _mirror(self, y):
+        return 2 * self.start + self.length - y
+
+    def _rotate(self, v, a):
+        """Rodrigues' rotation of rows of v around n by per-row angles a."""
+        n = self.n
+        cos, sin = np.cos(a)[:, None], np.sin(a)[:, None]
+        cross = np.cross(np.broadcast_to(n, v.shape), v)
+        dot = (v @ n)[:, None]
+        return v * cos + cross * sin + n * dot * (1 - cos)
+
+    def _from_start(self, p):
+        along = (p[:, 1] - self.start) / self.length * self.segments
+        ring = np.clip(np.floor(along).astype(int), 0, self.segments - 1)
+        f = np.clip(along - ring, 0, 1)
+        extra = np.where(along <= 0, p[:, 1] - self.start,
+                         np.where(along >= self.segments, p[:, 1] - (self.start + self.length), 0))
+        f = np.where(along <= 0, 0, np.where(along >= self.segments, 1, f))
+        center = self.ring[ring] + (self.ring[ring + 1] - self.ring[ring]) * f[:, None]
+        a = self.ring_angle[ring] + (self.ring_angle[ring + 1] - self.ring_angle[ring]) * f
+        local = np.stack([p[:, 0], extra, p[:, 2]], axis=1)
+        return self._rotate(local, a) + center
+
+    def bend_points(self, p):
+        if self.anchored_at_end:
+            q = p.copy()
+            q[:, 1] = self._mirror(q[:, 1])
+            out = self._from_start(q)
+            out[:, 1] = self._mirror(out[:, 1])
+            return out
+        return self._from_start(p)
+
+    def _angle_from_start(self, y):
+        along = np.clip((y - self.start) / self.length * self.segments, 0, self.segments)
+        ring = min(int(along), self.segments - 1)
+        return self.ring_angle[ring] + (self.ring_angle[ring + 1] - self.ring_angle[ring]) * (along - ring)
+
+    def angle_at(self, y):
+        return -self._angle_from_start(self._mirror(y)) if self.anchored_at_end else self._angle_from_start(y)
+
+
+def limb_bend(bone, t):
+    if t is None or t.bend == 0:
+        return None
+    if bone in ("right_arm", "left_arm"):
+        return LimbBend(-2, 10, t.bend, t.axis)
+    if bone in ("right_leg", "left_leg"):
+        return LimbBend(0, 12, t.bend, t.axis)
+    if bone == "body":
+        return LimbBend(0, 12, t.bend, t.axis, anchored_at_end=True)
+    return None
+
+
+def textured_points(bone, skin, spacing=0.5):
+    """Points on a part's base and outer layer with their skin colours, in the part's own space.
+
+    Follows ModelPart.Cube: each face's corners and UV rectangle, and Polygon's corner-to-UV order.
+    Outer layer points are only kept where the skin has pixels there.
+    """
+    (fx, fy, fz), (dx, dy, dz) = BOXES[bone]
+    pts, cols = [], []
+    for layer, (u, v) in enumerate(UV[bone]):
+        g = OVERLAY[bone] if layer else 0
+        f, gg, h = fx - g, fy - g, fz - g
+        i, j, k = fx + dx + g, fy + dy + g, fz + dz + g
+        c = [(f, gg, h), (i, gg, h), (i, j, h), (f, j, h), (f, gg, k), (i, gg, k), (i, j, k), (f, j, k)]
+        l, m, n, o = u, u + dz, u + dz + dx, u + dz + dx + dx
+        pp, q = u + dz + dx + dz, u + dz + dx + dz + dx
+        r, ss, t = v, v + dz, v + dz + dy
+        faces = [  # corners, (u1, v1, u2, v2)
+            ((5, 4, 0, 1), (m, r, n, ss)),   # DOWN: minY, the top in model space
+            ((2, 3, 7, 6), (n, ss, o, r)),   # UP: maxY, the bottom
+            ((0, 4, 7, 3), (l, ss, m, t)),   # WEST: -X, the player's right
+            ((1, 0, 3, 2), (m, ss, n, t)),   # NORTH: -Z, the front
+            ((5, 1, 2, 6), (n, ss, pp, t)),  # EAST: +X, the player's left
+            ((4, 5, 6, 7), (pp, ss, q, t)),  # SOUTH: +Z, the back
+        ]
+        for corners, (u1, v1, u2, v2) in faces:
+            a, b, _, d = (np.array(c[x], float) for x in corners)
+            ns = max(2, int(np.ceil(np.linalg.norm(b - a) / spacing)) + 1)
+            nt = max(2, int(np.ceil(np.linalg.norm(d - a) / spacing)) + 1)
+            # Sample cell centres so every point falls inside one texture pixel.
+            sv, tv = np.meshgrid((np.arange(ns) + 0.5) / ns, (np.arange(nt) + 0.5) / nt, indexing="ij")
+            sv, tv = sv.ravel(), tv.ravel()
+            face = a + sv[:, None] * (b - a) + tv[:, None] * (d - a)
+            tu = np.clip(np.floor(u2 + sv * (u1 - u2)).astype(int), min(u1, u2), max(u1, u2) - 1)
+            tvv = np.clip(np.floor(v1 + tv * (v2 - v1)).astype(int), min(v1, v2), max(v1, v2) - 1)
+            rgba = skin[tvv, tu]
+            keep = rgba[:, 3] > 0 if layer else np.ones(len(face), bool)
+            pts.append(face[keep])
+            cols.append(rgba[keep, :3])
+    return np.concatenate(pts), np.concatenate(cols).astype(float) / 255
+
+
+def surface_points(bone, spacing=0.5, overlay=True):
+    """Points on the surface of a part's box (outer layer included), in the part's own space."""
+    (fx, fy, fz), (sx, sy, sz) = BOXES[bone]
+    g = OVERLAY[bone] if overlay else 0
+    lo = np.array([fx - g, fy - g, fz - g])
+    hi = np.array([fx + sx + g, fy + sy + g, fz + sz + g])
+    axes = [np.linspace(lo[i], hi[i], max(2, int(np.ceil((hi[i] - lo[i]) / spacing)) + 1)) for i in range(3)]
+    pts = []
+    for fixed in range(3):
+        a, b = [i for i in range(3) if i != fixed]
+        u, v = np.meshgrid(axes[a], axes[b], indexing="ij")
+        for side in (lo[fixed], hi[fixed]):
+            face = np.empty((u.size, 3))
+            face[:, a], face[:, b], face[:, fixed] = u.ravel(), v.ravel(), side
+            pts.append(face)
+    return np.unique(np.round(np.concatenate(pts), 4), axis=0)
+
+
+class Model:
+    """Poses the player's parts. pose() returns model-space points for each part.
+
+    With a skin (an RGBA array), the points carry the skin's colours in self.colors, and the outer layer
+    only exists where the skin has pixels. Without one, parts are plain boxes including the outer layer.
+    """
+
+    def __init__(self, spacing=0.5, skin=None):
+        if skin is None:
+            self.local = {bone: surface_points(bone, spacing) for bone in PIVOTS}
+            self.colors = None
+        else:
+            textured = {bone: textured_points(bone, skin, spacing) for bone in PIVOTS}
+            self.local = {bone: pts for bone, (pts, _) in textured.items()}
+            self.colors = np.concatenate([cols for _, cols in textured.values()])
+
+    def part_frames(self, pose):
+        """Each part's pivot, rotation and bend after PoseApplier.apply, in root space."""
+        frames = {}
+        for bone in PIVOTS:
+            t = pose.get(bone)
+            pivot = np.array(PIVOTS[bone], float)
+            rot = np.eye(3)
+            if t is not None:
+                pivot = pivot + [t.x, t.y, t.z]
+                rot = rotation_zyx(t.pitch, t.yaw, t.roll)
+            frames[bone] = [pivot, rot, limb_bend(bone, t)]
+
+        body_pivot, body_rot, body_bend = frames["body"]
+        if body_bend is not None and not body_bend.straight:
+            for bone in ("head", "right_arm", "left_arm"):
+                pivot, rot, bend = frames[bone]
+                local = body_rot.T @ (pivot - body_pivot)
+                moved = body_bend.bend_points(local[None, :])[0]
+                turn = axis_angle(body_bend.n, body_bend.angle_at(local[1]))
+                frames[bone] = [body_pivot + body_rot @ moved, body_rot @ turn @ body_rot.T @ rot, bend]
+        return frames
+
+    def pose(self, pose):
+        frames = self.part_frames(pose)
+        root = pose.get("root")
+        if root is not None:
+            r_rot = rotation_zyx(root.pitch, root.yaw, root.roll)
+            pivot = np.array([0, ROOT_PIVOT_Y, 0])
+            r_pos = np.array([root.x, root.y, root.z]) + pivot - r_rot @ pivot
+        else:
+            r_rot, r_pos = np.eye(3), np.zeros(3)
+        out = {}
+        for bone, (pivot, rot, bend) in frames.items():
+            p = self.local[bone]
+            if bend is not None and not bend.straight:
+                p = bend.bend_points(p)
+            out[bone] = (p @ rot.T + pivot) @ r_rot.T + r_pos
+        return out
+
+
+# --- The rig the fitter works in -------------------------------------------------------------------
+#
+# Moving every pivot freely lets parts drift apart, so the fitter uses a jointed rig instead and
+# converts it to BoneTransforms: the legs hang from the hips, the torso's hip end sits on the hips,
+# and the head and arms hang from the torso (PoseApplier then carries them along its bend).
+
+RIG = (
+    ["root_x", "root_y", "root_z", "root_pitch", "root_yaw", "root_roll", "hip_x", "hip_y", "hip_z"]
+    + [f"body_{c}" for c in ("pitch", "yaw", "roll", "bend", "axis")]
+    + [f"head_{c}" for c in ("pitch", "yaw", "roll")]
+    + [f"{limb}_{c}" for limb in ("right_arm", "left_arm", "right_leg", "left_leg")
+       for c in ("pitch", "yaw", "roll", "bend", "axis")]
+)
+INDEX = {name: i for i, name in enumerate(RIG)}
+
+
+def rig_to_pose(v):
+    g = lambda name: float(v[INDEX[name]])
+    hip = np.array([g("hip_x"), g("hip_y"), g("hip_z")])
+    body_rot = rotation_zyx(g("body_pitch"), g("body_yaw"), g("body_roll"))
+    # The torso's bottom middle (0, 12, 0) in its own space stays on the hips; a bend keeps it fixed.
+    hips_rest = np.array([0, 12, 0.0])
+    body_pivot = hips_rest + hip - body_rot @ hips_rest
+    pose = {
+        "root": Transform(g("root_x"), g("root_y"), g("root_z"), g("root_pitch"), g("root_yaw"), g("root_roll")),
+        "body": Transform(*body_pivot, g("body_pitch"), g("body_yaw"), g("body_roll"), g("body_bend"), g("body_axis")),
+        "head": Transform(*body_pivot, g("head_pitch"), g("head_yaw"), g("head_roll")),
+    }
+    for arm in ("right_arm", "left_arm"):
+        rest = np.array(PIVOTS[arm], float)
+        # Where the shoulder sits on the (unbent) tipped torso; PoseApplier adds the bend's carry.
+        offset = body_pivot + body_rot @ rest - rest
+        pose[arm] = Transform(*offset, *(g(f"{arm}_{c}") for c in ("pitch", "yaw", "roll", "bend", "axis")))
+    for leg in ("right_leg", "left_leg"):
+        pose[leg] = Transform(*hip, *(g(f"{leg}_{c}") for c in ("pitch", "yaw", "roll", "bend", "axis")))
+    return pose
+
+
+def rest_rig():
+    return np.zeros(len(RIG))
diff --git a/tools/capture/overlay.py b/tools/capture/overlay.py
new file mode 100644
index 0000000..83b8249
--- /dev/null
+++ b/tools/capture/overlay.py
@@ -0,0 +1,63 @@
+"""Draws the fitted pose's outline over the captured frames, to see where the fit is off.
+
+    .venv/bin/python overlay.py <name> [--ticks 0:13:3] [--views y000,y090,y045,top000]
+
+Writes frames/<name>/overlay.png: rows are views, columns ticks. Red is where only our model is, blue
+where only theirs is, and grey where both are.
+"""
+import argparse
+import json
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+
+from model import Model, rig_to_pose
+from render import Camera, render_masks
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("name")
+    parser.add_argument("--ticks", default="0:13:3")
+    parser.add_argument("--views", default="y000,y090,y045,top000")
+    args = parser.parse_args()
+
+    base = Path("frames") / args.name
+    meta = json.loads((base / "views.json").read_text())
+    data = json.loads((base / "rig.json").read_text())
+    by_tick = {t["tick"]: np.array(t["rig"]) for t in data["ticks"]}
+    ticks = [t for t in range(*map(int, args.ticks.split(":"))) if t in by_tick]
+    views = args.views.split(",")
+    scale = 2
+    cams = {v: Camera(meta["views"][v], meta["size"], meta["aspect"], scale) for v in views}
+    model = Model(spacing=0.25)
+
+    tiles = []
+    for v in views:
+        row = []
+        for t in ticks:
+            pts = np.concatenate(list(model.pose(rig_to_pose(by_tick[t])).values()))
+            ours = render_masks(pts, data["calib"], {v: cams[v]})[v]
+            frame = Image.open(base / v / f"{t:04d}.png").reduce(scale)
+            theirs = np.asarray(frame)[:, :, 3] > 127
+            img = np.full(ours.shape + (3,), 30, np.uint8)
+            img[theirs & ours] = (150, 150, 150)
+            img[ours & ~theirs] = (230, 60, 60)
+            img[theirs & ~ours] = (60, 110, 240)
+            row.append(img)
+        tiles.append(row)
+
+    both = np.zeros(tiles[0][0].shape[:2], bool)
+    for row in tiles:
+        for img in row:
+            both |= img.sum(axis=2) > 90
+    ys, xs = np.nonzero(both)
+    y0, y1, x0, x1 = max(ys.min() - 4, 0), ys.max() + 4, max(xs.min() - 4, 0), xs.max() + 4
+    sheet = np.concatenate([np.concatenate([img[y0:y1, x0:x1] for img in row], axis=1) for row in tiles], axis=0)
+    Image.fromarray(sheet).save(base / "overlay.png")
+    print("wrote", base / "overlay.png", "ticks", ticks)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tools/capture/render.py b/tools/capture/render.py
new file mode 100644
index 0000000..20aa95a
--- /dev/null
+++ b/tools/capture/render.py
@@ -0,0 +1,174 @@
+"""Renders model points through the cameras capture.py used, and scores them against captured masks."""
+import json
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+from scipy.ndimage import distance_transform_edt
+
+# Model space (Y down, facing -Z) to the viewer's world (Y up, facing +Z): Minecraft's scale(-1, -1, 1)
+# after turning the player 180 degrees to face south.
+FLIP = np.diag([1.0, -1.0, -1.0])
+
+
+class Camera:
+    """A three.js PerspectiveCamera placed with lookAt, as viewer.js places it."""
+
+    def __init__(self, view, size, aspect, scale):
+        yaw, pitch = np.radians(view["yaw"]), np.radians(view["pitch"])
+        target = np.array(view["target"], float)
+        d = view["distance"]
+        self.position = target + d * np.array([np.cos(pitch) * np.sin(yaw), np.sin(pitch), np.cos(pitch) * np.cos(yaw)])
+        z = self.position - target
+        z /= np.linalg.norm(z)
+        x = np.cross([0, 1, 0], z)
+        x /= np.linalg.norm(x)
+        y = np.cross(z, x)
+        self.rotation = np.stack([x, y, z])  # world to camera
+        self.f = 1 / np.tan(np.radians(view["fov"]) / 2)
+        self.aspect = aspect
+        self.width, self.height = size[0] // scale, size[1] // scale
+
+    def project(self, world):
+        c = (world - self.position) @ self.rotation.T
+        depth = -c[:, 2]
+        nx = self.f / self.aspect * c[:, 0] / depth
+        ny = self.f * c[:, 1] / depth
+        return np.stack([(nx + 1) / 2 * self.width, (1 - ny) / 2 * self.height], axis=1)
+
+
+def to_world(points, calib):
+    """calib = (scale, tx, ty, tz): where the viewer puts the model."""
+    return calib[0] * points @ FLIP.T + np.asarray(calib[1:4])
+
+
+def splat(px, width, height):
+    """A mask covering every projected point, 2x2 pixels each so the surface has no holes."""
+    mask = np.zeros((height, width), bool)
+    x = np.floor(px[:, 0] - 0.5).astype(int)
+    y = np.floor(px[:, 1] - 0.5).astype(int)
+    for dx in (0, 1):
+        for dy in (0, 1):
+            xs, ys = x + dx, y + dy
+            ok = (xs >= 0) & (xs < width) & (ys >= 0) & (ys < height)
+            mask[ys[ok], xs[ok]] = True
+    return mask
+
+
+def color_features(rgb):
+    """Lighting-tolerant colour: hue and saturation as opponent channels divided by brightness, plus
+    log brightness. Shading scales all three channels, which leaves the first two alone."""
+    lum = rgb.mean(axis=-1)
+    o1 = rgb[..., 0] - rgb[..., 1]
+    o2 = (rgb[..., 0] + rgb[..., 1]) / 2 - rgb[..., 2]
+    return np.stack([o1 / (lum + 0.05), o2 / (lum + 0.05), np.log(lum + 0.05)], axis=-1)
+
+
+class Target:
+    """One captured view of one tick, downscaled, with the distance maps the score needs."""
+
+    def __init__(self, mask, rgb=None):
+        self.mask = mask
+        self.outside = distance_transform_edt(~mask)
+        self.ys, self.xs = np.nonzero(mask)
+        self.features = None if rgb is None else color_features(rgb)
+
+
+def downscale(alpha, scale):
+    h, w = alpha.shape
+    blocks = alpha[: h // scale * scale, : w // scale * scale].reshape(h // scale, scale, w // scale, scale)
+    return blocks.mean(axis=(1, 3)) > 127
+
+
+def downscale_rgb(rgba, scale):
+    """Mean colour of the covered pixels in each block (0-1)."""
+    h, w = rgba.shape[:2]
+    blocks = rgba[: h // scale * scale, : w // scale * scale].astype(float)
+    blocks = blocks.reshape(h // scale, scale, w // scale, scale, 4)
+    alpha = blocks[..., 3:] / 255
+    return (blocks[..., :3] / 255 * alpha).sum(axis=(1, 3)) / np.maximum(alpha.sum(axis=(1, 3)), 1e-6)
+
+
+class Capture:
+    """The frames and cameras of one capture."""
+
+    def __init__(self, directory, scale=4, views=None):
+        self.dir = Path(directory)
+        self.meta = json.loads((self.dir / "views.json").read_text())
+        self.scale = scale
+        self.names = views or list(self.meta["views"])
+        self.cameras = {n: Camera(self.meta["views"][n], self.meta["size"], self.meta["aspect"], scale) for n in self.names}
+
+    def targets(self, tick):
+        out = {}
+        for n in self.names:
+            rgba = np.asarray(Image.open(self.dir / n / f"{tick:04d}.png"))
+            out[n] = Target(downscale(rgba[:, :, 3], self.scale), downscale_rgb(rgba, self.scale))
+        return out
+
+
+# How much a unit of colour difference counts against a pixel of outline distance, and how much
+# brightness counts next to hue and saturation.
+COLOR_WEIGHT = 0.5
+BRIGHTNESS_WEIGHT = 0.3
+
+
+def score(points, calib, cameras, targets, per_view=False, colors=None, color_views=None, one_way=False,
+          color_weight=None):
+    """Mean outline distance in pixels, both ways (our points outside theirs and theirs outside ours),
+    plus, with colours, how different the colours are where both outlines cover a pixel.
+
+    one_way only counts our points outside theirs, for fitting some of the parts: the rest of their
+    outline is still unexplained, and shouldn't pull the parts being fitted towards it."""
+    world = to_world(points, calib)
+    total = {}
+    for name, cam in cameras.items():
+        t = targets[name]
+        px = cam.project(world)
+        xi = np.clip(px[:, 0].astype(int), 0, cam.width - 1)
+        yi = np.clip(px[:, 1].astype(int), 0, cam.height - 1)
+        ours_out = t.outside[yi, xi].mean()
+        if colors is not None and t.features is not None and (color_views is None or name in color_views):
+            depth = -((world - cam.position) @ cam.rotation.T)[:, 2]
+            image, mask = splat_color(px, depth, colors, cam.width, cam.height)
+            both = mask & t.mask
+            diff = np.abs(color_features(image[both]) - t.features[both])
+            color = (diff[:, 0] + diff[:, 1] + BRIGHTNESS_WEIGHT * diff[:, 2]).mean() if both.any() else 0
+        else:
+            mask = splat(px, cam.width, cam.height)
+            color = 0
+        theirs_out = 0 if one_way or not len(t.ys) else distance_transform_edt(~mask)[t.ys, t.xs].mean()
+        total[name] = ours_out + theirs_out + (COLOR_WEIGHT if color_weight is None else color_weight) * color
+    return total if per_view else sum(total.values()) / len(total)
+
+
+def render_masks(points, calib, cameras):
+    world = to_world(points, calib)
+    return {n: splat(c.project(world), c.width, c.height) for n, c in cameras.items()}
+
+
+def splat_color(px, depth, colors, width, height):
+    """Colour image of the nearest point in each pixel (2x2 pixels per point), and its coverage mask."""
+    order = np.argsort(-depth)  # far to near, so nearer points are written last and win
+    x = np.floor(px[order, 0] - 0.5).astype(int)
+    y = np.floor(px[order, 1] - 0.5).astype(int)
+    # All four pixels of every point in one write; a write per offset would let far points overwrite
+    # pixels near points filled at another offset.
+    xs = np.stack([x, x + 1, x, x + 1], axis=1).ravel()
+    ys = np.stack([y, y, y + 1, y + 1], axis=1).ravel()
+    cs = np.repeat(colors[order], 4, axis=0)
+    ok = (xs >= 0) & (xs < width) & (ys >= 0) & (ys < height)
+    image = np.zeros((height, width, 3))
+    mask = np.zeros((height, width), bool)
+    image[ys[ok], xs[ok]] = cs[ok]
+    mask[ys[ok], xs[ok]] = True
+    return image, mask
+
+
+def render_colors(points, colors, calib, cameras):
+    world = to_world(points, calib)
+    out = {}
+    for n, c in cameras.items():
+        depth = -((world - c.position) @ c.rotation.T)[:, 2]
+        out[n] = splat_color(c.project(world), depth, colors, c.width, c.height)
+    return out
diff --git a/tools/capture/requirements.txt b/tools/capture/requirements.txt
new file mode 100644
index 0000000..9f27a86
--- /dev/null
+++ b/tools/capture/requirements.txt
@@ -0,0 +1,5 @@
+cma==4.5.0
+numpy==2.5.3
+pillow==12.3.0
+playwright==1.63.0
+scipy==1.18.1
diff --git a/tools/capture/sheet.py b/tools/capture/sheet.py
new file mode 100644
index 0000000..b917e2b
--- /dev/null
+++ b/tools/capture/sheet.py
@@ -0,0 +1,20 @@
+"""Tiles frames into a contact sheet: rows are views, columns are ticks, cropped to the player."""
+import sys
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+
+name, views, ticks = sys.argv[1], sys.argv[2].split(","), range(*map(int, sys.argv[3].split(":")))
+out = sys.argv[4] if len(sys.argv) > 4 else "sheet.png"
+frames = [[Image.open(Path("frames") / name / v / f"{t:04d}.png") for t in ticks] for v in views]
+alpha = np.maximum.reduce([np.asarray(f)[:, :, 3] for row in frames for f in row])
+ys, xs = np.nonzero(alpha)
+box = (xs.min() - 4, ys.min() - 4, xs.max() + 4, ys.max() + 4)
+w, h = box[2] - box[0], box[3] - box[1]
+sheet = Image.new("RGBA", (w * len(ticks), h * len(views)), (40, 40, 52, 255))
+for r, row in enumerate(frames):
+    for c, f in enumerate(row):
+        sheet.alpha_composite(f.crop(box), (c * w, r * h))
+sheet.save(out)
+print(sheet.size)
diff --git a/tools/capture/skin.py b/tools/capture/skin.py
new file mode 100644
index 0000000..3f2c889
--- /dev/null
+++ b/tools/capture/skin.py
@@ -0,0 +1,29 @@
+"""Fetches a player's skin texture from Mojang, the same one the store viewer shows for that username.
+
+    .venv/bin/python skin.py <username> [out.png]
+"""
+import base64
+import json
+import subprocess
+import sys
+
+
+def get(url):
+    # curl uses the system's certificate store, which python.org's Python doesn't.
+    return subprocess.run(["curl", "-sSf", url], capture_output=True, check=True).stdout
+
+
+def fetch(username):
+    """Returns (png bytes, slim arms)."""
+    uuid = json.loads(get(f"https://api.mojang.com/users/profiles/minecraft/{username}"))["id"]
+    profile = json.loads(get(f"https://sessionserver.mojang.com/session/minecraft/profile/{uuid}"))
+    prop = next(p for p in profile["properties"] if p["name"] == "textures")
+    skin = json.loads(base64.b64decode(prop["value"]))["textures"]["SKIN"]
+    return get(skin["url"]), skin.get("metadata", {}).get("model") == "slim"
+
+
+if __name__ == "__main__":
+    png, slim = fetch(sys.argv[1])
+    out = sys.argv[2] if len(sys.argv) > 2 else f"skins/{sys.argv[1]}.png"
+    open(out, "wb").write(png)
+    print(out, "slim" if slim else "wide")
diff --git a/tools/capture/symmetry.py b/tools/capture/symmetry.py
new file mode 100644
index 0000000..bb2ba9c
--- /dev/null
+++ b/tools/capture/symmetry.py
@@ -0,0 +1,110 @@
+"""Left/right symmetry of a measured loop: how lopsided it is, and a symmetric version of it.
+
+    .venv/bin/python symmetry.py <name> [--rig path.json]
+
+A mirror swaps the left and right limbs and flips every sideways channel (the hips' x, yaws, rolls and
+bend directions). A pose that sways once per loop should be the mirror of itself half a loop later;
+one with no sideways motion, the mirror of itself at the same moment. Whatever breaks that is
+lopsidedness the fit picked up (one leg bent more, one side swinging further), not the dance.
+
+symmetrize() averages each tick with the mirror of its partner, which keeps the sway and removes the
+rest. fit.py --symmetric uses it.
+"""
+import argparse
+import json
+from pathlib import Path
+
+import numpy as np
+
+from model import INDEX, RIG
+
+SIDES = {"right_arm": "left_arm", "left_arm": "right_arm", "right_leg": "left_leg", "left_leg": "right_leg"}
+ANGLES = np.array([not name.endswith(("_x", "_y", "_z")) for name in RIG])
+
+
+def wrap(a):
+    return (a + np.pi) % (2 * np.pi) - np.pi
+
+
+def mirror(rig):
+    """The pose reflected through the player's middle (x -> -x). Rotations about x keep their angle and
+    those about y and z flip (S Rz Ry Rx S with S = diag(-1, 1, 1)); a bend direction, measured around
+    the limb from its front, flips too."""
+    out = np.empty_like(rig)
+    for i, name in enumerate(RIG):
+        part, _, channel = name.rpartition("_")
+        source = next((f"{SIDES[p]}{name[len(p):]}" for p in SIDES if name.startswith(p)), name)
+        value = rig[INDEX[source]]
+        out[i] = -value if channel in ("x", "yaw", "roll", "axis") else value
+    return out
+
+
+def shifted(rigs, fraction):
+    """Each channel of a loop evaluated `fraction` of a loop later, between samples too, through its
+    Fourier series. Angles that go round whole turns in a loop keep doing so."""
+    rigs = np.asarray(rigs, float)
+    n = len(rigs)
+    out = np.empty_like(rigs)
+    k = np.arange(n // 2 + 1)
+    for i in range(rigs.shape[1]):
+        values = np.unwrap(rigs[:, i]) if ANGLES[i] else rigs[:, i]
+        turns = 0.0
+        if ANGLES[i]:
+            turns = values[-1] + wrap(values[0] - values[-1]) - values[0]
+        ramp = turns * np.arange(n) / n
+        spectrum = np.fft.rfft(values - ramp) * np.exp(2j * np.pi * k * fraction)
+        out[:, i] = np.fft.irfft(spectrum, n) + ramp + turns * fraction
+    return out
+
+
+def partners(rigs, sway):
+    """The mirrored partner of every tick: half a loop on for a pose that sways once per loop."""
+    rigs = np.asarray(rigs, float)
+    source = shifted(rigs, 0.5) if sway else rigs
+    return np.array([mirror(r) for r in source])
+
+
+def difference(a, b):
+    d = a - b
+    return np.where(ANGLES, wrap(d), d)
+
+
+def symmetrize(rigs, sway):
+    """Each tick averaged with its mirrored partner (the short way round for angles)."""
+    rigs = np.asarray(rigs, float)
+    return rigs - difference(rigs, partners(rigs, sway)) / 2
+
+
+def lopsidedness(rigs, sway):
+    """Per channel: how far each tick is from its mirrored partner, as (mean, rms) over the loop. The
+    mean is a steady lean to one side; the rms includes one side swinging further than the other."""
+    d = difference(np.asarray(rigs, float), partners(rigs, sway))
+    return d.mean(axis=0), np.sqrt((d ** 2).mean(axis=0))
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("name")
+    parser.add_argument("--rig")
+    args = parser.parse_args()
+    path = Path(args.rig) if args.rig else Path("frames") / args.name / "rig.json"
+    rigs = np.array([t["rig"] for t in json.loads(path.read_text())["ticks"]])
+
+    # Which pairing fits: a sway once per loop, or none.
+    for sway in (True, False):
+        _, rms = lopsidedness(rigs, sway)
+        angle_rms = np.degrees(np.sqrt((rms[ANGLES] ** 2).mean()))
+        offset_rms = np.sqrt((rms[~ANGLES] ** 2).mean())
+        print(f"{'sway once per loop' if sway else 'no sway':18s}: rms {angle_rms:.1f} deg, {offset_rms:.2f} px")
+
+    sway = True
+    mean, rms = lopsidedness(rigs, sway)
+    print("\nMost lopsided channels (tick vs its mirrored partner half a loop on):")
+    order = np.argsort(-np.where(ANGLES, np.degrees(rms), rms * 5))
+    for i in order[:14]:
+        unit, f = ("deg", np.degrees) if ANGLES[i] else ("px", lambda v: v)
+        print(f"  {RIG[i]:18s} steady {f(mean[i]):+6.1f} {unit}, rms {f(rms[i]):5.1f} {unit}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tools/capture/synth_test.py b/tools/capture/synth_test.py
new file mode 100644
index 0000000..a13c019
--- /dev/null
+++ b/tools/capture/synth_test.py
@@ -0,0 +1,85 @@
+"""End-to-end check of measure.py on frames with a known answer: our own twerk test animation, rendered
+by model.py as if it were a capture, then measured like one.
+
+    .venv/bin/python synth_test.py make <capture to copy cameras from> [ticks]   # writes frames/synth
+    .venv/bin/python measure.py synth --ticks 0:4
+    .venv/bin/python synth_test.py check
+"""
+import json
+import sys
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+from scipy.spatial import cKDTree
+
+from model import INDEX, Model, rest_rig, rig_to_pose
+from render import Camera, render_colors
+
+# Where the fake viewer puts the model: Minecraft's player scale, feet 25 units below the origin.
+CALIB = (0.9375, 0, -1.5, 0)
+SKIN = "Steve"
+OUT = Path("frames/synth")
+
+
+def twerk(t):
+    """TestAnimations.twerk, written in rig terms."""
+    up = 0.5 * (1 - np.cos(t / 8 * 2 * np.pi))
+    thigh, knee, splay, tilt, arch = -1.1 + 0.15 * up, 1.75 - 0.3 * up, 0.55, 1.4, 0.4 + 0.3 * up
+    drop = 12 - 6 * (np.cos(thigh) + np.cos(thigh + knee)) * np.cos(splay)
+    back = -6 * (np.sin(thigh) + np.sin(thigh + knee))
+    chest = tilt - arch
+    v = rest_rig()
+    s = lambda name, value: v.__setitem__(INDEX[name], value)
+    s("hip_y", drop), s("hip_z", back)
+    s("body_pitch", tilt), s("body_bend", arch), s("body_axis", np.pi)
+    s("head_pitch", tilt - chest * 0.5)
+    for arm, roll in (("right_arm", 0.3), ("left_arm", -0.3)):
+        s(f"{arm}_pitch", tilt - chest * 1.1), s(f"{arm}_roll", roll), s(f"{arm}_bend", 0.5)
+    for leg, sign in (("right_leg", 1), ("left_leg", -1)):
+        s(f"{leg}_pitch", thigh), s(f"{leg}_roll", sign * splay), s(f"{leg}_bend", knee)
+        s(f"{leg}_axis", np.pi - sign * 0.35)
+    return v
+
+
+def skin():
+    return np.asarray(Image.open(f"skins/{SKIN}.png").convert("RGBA"))
+
+
+def make(source, ticks):
+    meta = json.loads((Path("frames") / source / "views.json").read_text())
+    model = Model(spacing=0.08, skin=skin())
+    cams = {n: Camera(v, meta["size"], meta["aspect"], 1) for n, v in meta["views"].items()}
+    for tick in range(ticks):
+        pts = np.concatenate(list(model.pose(rig_to_pose(twerk(tick))).values()))
+        for name, (image, mask) in render_colors(pts, model.colors, CALIB, cams).items():
+            (OUT / name).mkdir(parents=True, exist_ok=True)
+            rgba = np.concatenate([image * 255, mask[..., None] * 255], axis=-1).astype(np.uint8)
+            Image.fromarray(rgba).save(OUT / name / f"{tick:04d}.png")
+    meta |= {"url": "synthetic twerk", "skin": SKIN, "ticks": ticks, "loop_ticks": None}
+    (OUT / "views.json").write_text(json.dumps(meta, indent=2))
+    print(f"wrote {ticks} ticks to {OUT}")
+
+
+def check():
+    data = json.loads((OUT / "rig.json").read_text())
+    print("calibration:", np.round(data["calib"], 3), "true:", CALIB)
+    model = Model(skin=skin())
+    for entry in data["ticks"]:
+        truth, got = model.pose(rig_to_pose(twerk(entry["tick"]))), model.pose(rig_to_pose(np.array(entry["rig"])))
+        # The fitted pose sits in the fitted calibration's frame; compare where the viewer shows them.
+        to = lambda p, c: c[0] * p + np.asarray(c[1:]) * [1, -1, -1]
+        parts = []
+        for part in truth:
+            a, b = to(truth[part], CALIB), to(got[part], data["calib"])
+            parts.append((part, np.linalg.norm(a - b, axis=1).mean(), cKDTree(b).query(a)[0].mean()))
+        print(f"tick {entry['tick']}: score {entry['score']:.3f}  "
+              + "  ".join(f"{p} {d:.2f}/{s:.2f}" for p, d, s in parts))
+    print("(per part: mean point-for-point / shape-only distance, in model pixels)")
+
+
+if __name__ == "__main__":
+    if sys.argv[1] == "make":
+        make(sys.argv[2], int(sys.argv[3]) if len(sys.argv) > 3 else 4)
+    else:
+        check()
diff --git a/tools/capture/viewer.js b/tools/capture/viewer.js
new file mode 100644
index 0000000..46a38ad
--- /dev/null
+++ b/tools/capture/viewer.js
@@ -0,0 +1,88 @@
+// Injected before the page loads. It puts the page on a clock we step by hand, and uses three.js's
+// devtools hook to find the renderer and the camera it draws with. Only the clock and the camera are
+// touched: the frames are measured from pixels, never from the scene's animation data.
+(() => {
+    let now = 0;
+    let callbacks = [];
+    let nextId = 1;
+
+    const realDateNow = Date.now.bind(Date);
+    const epoch = realDateNow();
+    performance.now = () => now;
+    Date.now = () => epoch + now;
+    window.requestAnimationFrame = (cb) => {
+        const id = nextId++;
+        callbacks.push({ id, cb });
+        return id;
+    };
+    window.cancelAnimationFrame = (id) => {
+        callbacks = callbacks.filter((c) => c.id !== id);
+    };
+
+    // The camera placement to force on every render: a point the camera orbits, its distance, and the
+    // yaw/pitch it looks from (degrees; yaw 0 looks at the model's front).
+    let view = null;
+    let lastFrame = null;
+
+    const capture = {
+        renderers: [],
+        camera: null,
+        /** Advances the clock by ms and runs one animation frame. Returns the frame as a PNG data URL. */
+        step(ms, grab) {
+            now += ms;
+            lastFrame = null;
+            const due = callbacks;
+            callbacks = [];
+            for (const { cb } of due) {
+                try { cb(now); } catch (e) { console.error(e); }
+            }
+            return grab ? lastFrame : null;
+        },
+        time() { return now; },
+        setView(v) { view = v; },
+        camera_info() {
+            const cam = capture.camera;
+            return cam && { fov: cam.fov, aspect: cam.aspect, near: cam.near, far: cam.far,
+                            position: cam.position.toArray(), quaternion: cam.quaternion.toArray() };
+        },
+    };
+    window.__capture = capture;
+
+    function place(camera) {
+        const toRad = Math.PI / 180;
+        const yaw = view.yaw * toRad;
+        const pitch = view.pitch * toRad;
+        const [tx, ty, tz] = view.target;
+        camera.position.set(
+            tx + view.distance * Math.cos(pitch) * Math.sin(yaw),
+            ty + view.distance * Math.sin(pitch),
+            tz + view.distance * Math.cos(pitch) * Math.cos(yaw));
+        camera.up.set(0, 1, 0);
+        camera.lookAt(tx, ty, tz);
+        if (view.fov) {
+            camera.fov = view.fov;
+        }
+        camera.updateProjectionMatrix();
+        camera.updateMatrixWorld(true);
+    }
+
+    const hook = new EventTarget();
+    hook.addEventListener('observe', (event) => {
+        const obj = event.detail;
+        if (obj && obj.isWebGLRenderer && !capture.renderers.includes(obj)) {
+            capture.renderers.push(obj);
+            const render = obj.render.bind(obj);
+            obj.render = (scene, camera) => {
+                capture.camera = camera;
+                if (view) {
+                    place(camera);
+                }
+                const result = render(scene, camera);
+                // Read the drawing buffer in the same task as the draw, before the browser clears it.
+                lastFrame = obj.domElement.toDataURL('image/png');
+                return result;
+            };
+        }
+    });
+    window.__THREE_DEVTOOLS__ = hook;
+})();