jeremy
31ba2911df
Skills (.claude/skills/): animation-creation, iclone-video-mocap, pose-estimation (MediaPipe models via LFS), retarget-animations (deprecated). Tools: cc/mixamo/kevin/mocap retargeters + composite baker. Plans: animation-gen-pipeline + skills-adoption. exchange/: incoming-fbx, converted-glb, reference-video (LFS for fbx/glb/mp4). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
80 lines
3.4 KiB
Markdown
80 lines
3.4 KiB
Markdown
---
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name: pose-estimation
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description: Extract human pose landmarks from video or images (MediaPipe Tasks API) and compare pose sequences with DTW joint-angle scoring. Use for analyzing dance/movement videos, scoring a recreated animation against source footage, or any "what is the body doing at time t" question. Works on real footage AND stylized game characters when a single subject fills the frame.
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---
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# Pose Estimation
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Two tested scripts, one venv. Extract 33-landmark pose sequences from video, then
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score two sequences against each other (e.g. source dance video vs game recreation).
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## Setup (idempotent, run once per machine)
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```bash
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bash ~/.claude/skills/pose-estimation/scripts/setup.sh
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```
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Creates `.venv` (pins python3.12 — MediaPipe does not support 3.13+) and downloads the
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`full` pose model. `lite`/`heavy` model variants auto-download on first use via curl
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(NOT urllib — macOS python lacks SSL certs; keep the curl download path).
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Always run scripts with the venv python: `~/.claude/skills/pose-estimation/.venv/bin/python`.
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## Extract poses
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```bash
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VENV=~/.claude/skills/pose-estimation/.venv/bin/python
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SKILL=~/.claude/skills/pose-estimation/scripts
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$VENV $SKILL/extract_pose.py input.mp4 --out poses.json --fps 8 \
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--overlay check.mp4 --min-conf 0.3 --model heavy
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```
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- `--fps N` sample rate; `--times "0.0,0.5,1.0"` instead samples an explicit beat grid
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(pass a musical beat grid for dance work — samples land on choreographically meaningful frames).
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- `--overlay out.mp4` draws the skeleton on sampled frames — ALWAYS eyeball this before
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trusting the data.
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- Output JSON has both `landmarks` (normalized image coords) and `world_landmarks`
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(meters, hip-centered). Use world landmarks for any comparison — they are camera- and
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scale-invariant.
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- The `OK: N/M samples had a detected pose` line is the health signal. Low N/M → see
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framing rules below.
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## Compare two pose sequences
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```bash
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$VENV $SKILL/compare_poses.py source_poses.json recreation_poses.json \
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--a-range 2.0:6.0 --b-range 0:4.0
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```
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Reduces each frame to 8 joint angles (elbows, shoulders, hips, knees), aligns the two
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sequences with DTW (tolerates tempo drift), prints JSON: `score_0_100`,
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`mean_angle_error_deg`, and `per_joint_error_deg` sorted worst-first (tells you WHERE
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the recreation diverges — e.g. arms right, legs wrong). Verified: identical input → 100.0;
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different dance segments → ~70.
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## Framing rules (the thing that actually determines success)
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MediaPipe pose is **single-person** and needs the subject to fill a large fraction of
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the frame. Verified findings:
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- Wide shot with many small figures (e.g. a game formation capture at 1600x900 with
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~80px characters): **0% detection**. This is framing, not the model.
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- Same video, one character crop-zoomed to fill frame (`ffmpeg -vf "crop=W:H:X:Y,scale=4x"`):
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**94% detection** — even on stylized low-poly game characters (Quaternius rigs).
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- Real photos/footage: works out of the box.
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So: for multi-person or wide footage, crop to one subject first:
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```bash
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ffmpeg -i wide.mp4 -vf "crop=140:180:350:420,scale=560:720:flags=lanczos" -an solo.mp4
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```
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Knobs when detection is weak: `--min-conf 0.15`, `--model heavy`, bigger crop upscale.
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## Typical workflow (video → analysis)
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1. Extract a frame, look at it, choose the crop for the subject.
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2. Extract with `--overlay`, check the overlay video, check detection %.
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3. Compare / analyze from `world_landmarks` or via compare_poses.py.
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