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by patch-gg
Motion capture for any creature from one AI video: two cameras in one frame, a DINOv2 joint reader, the rig fitted in both views, a GLB out.
# Add to your Claude Code skills
git clone https://github.com/patch-gg/anymoveSee how anymove compares with popular alternatives.
anymove is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by patch-gg. Motion capture for any creature from one AI video: two cameras in one frame, a DINOv2 joint reader, the rig fitted in both views, a GLB out. It has 135 GitHub stars.
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Clone the repository with "git clone https://github.com/patch-gg/anymove" and add it to your Claude Code skills directory (see the Installation section above).
anymove is primarily written in Python. It is open-source under patch-gg on GitHub, so you can review or fork the full source.
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@patch_gg · dragon, giant spider, fire demon, wolf · one AI clip per move, $0.26 each, read and fitted in under a minute.
anymove turns one AI video into an animation for any rigged creature, wings, tails and eight legs included. A video model films the creature from two cameras in one frame, front on the left and side on the right. A joint reader trained on that one creature finds every joint in both views. A joint sits at the same height in both, so the pair pins it in 3D, and the rig is fitted to those joints frame by frame and exported as a GLB.
The method is @CanItCode's; the MiniMax H3 LoRA step is @Graalitoo's idea. This repo is our working version of both, with the agent skills to run it.
| 1 · a 3d model.One picture becomes a textured mesh at real size. flux 3 picture, tripo h3.1 mesh · $0.40 | |
| 2 · a skeleton.Bones placed on grid views, skinned in Blender.42 bones: legs, wings, tail | |
| 3 · limb colours.Every limb its own colour, so left and right never swap.6 limbs, 6 colours | |
| 4 · two cameras.One camera at the front, one at the side, both views in one frame; a video model animates both at once from a prompt. minimax h3 max on fal · 5 s, $0.26 | |
| 5 · teach a reader.A vision model learns this one dragon from random poses whose joints are known; its average miss falls from 40 px to 6 px. dinov2 · 2,200 poses · 20 min on one gpu | |
| 6 · read every joint.The reader finds every joint in both views, in every frame.50 joints · 10 s a clip | |
| 7 · two views, 3d.Stand the two views up as walls at a right angle: each joint is where its two lines meet. The front gives side to side, the side gives nose to tail.x from the front, y from the side, z from both | |
| 8 · the rig follows.The rig bends its bones onto the points, ignores the strays, and exports for any engine.glb, 24 fps, no hand edits · under a minute a clip |
docs/PIPELINE.md has the file-by-file version.
You need an NVIDIA GPU with CUDA (24 GB to train a reader; reading and fitting need much less), Python 3.11,
Blender 5.x on your PATH (or BLENDER=/path/to/blender), and a fal key
to make new clips.
git clone https://github.com/Patch-gg/anymove && cd anymove
python3.11 -m venv .venv
.venv/bin/pip install torch==2.11.0 --index-url https://download.pytorch.org/whl/cu128
.venv/bin/pip install -r requirements.txt
cp .env.example .env # then add your FAL_KEY; git ignores .env
Fit the dragon's roar on the CPU, no Blender or downloads needed. The repo carries the joints its reader found in the clip; this fits the rig to them (about 20 seconds) and prints how well the two views agree:
mkdir -p runs/dragon/clips/roar
MOCAP_CREATURE=assets/dragon .venv/bin/python -m mocap.solve examples/dragon_roar/det.npz \
assets/dragon/rigged/rig_info.json runs/dragon/clips/roar/motion.json
The whole capture, from the AI clip to a GLB, with an example creature's mesh, trained reader and clip from the release (13 MB mesh, 355 MB reader, 4 MB clip):
tools/fetch_examples.sh dragon --readers --clips
tools/prep.sh dragon && tools/rig.sh dragon # real size, skeleton, skin, limb paint, reference frame
tools/capture.sh dragon runs/dragon/videos/roar/s1/h3max.mp4 roar
# runs/dragon/clips/roar/render/animation.glb, and showcase.mp4 to compare it with the clip
A new move for the same dragon (two clips, about $0.52 on fal):
.venv/bin/python tools/make_prompt.py assets/dragon "the dragon whips its tail from side to side, then stands still." prompts/dragon/tailwhip.txt
tools/film.sh dragon tailwhip h3max 1,2
tools/capture.sh dragon runs/dragon/videos/tailwhip/s1/h3max.mp4 tailwhip
Copy the closest example in assets/ (wolf: four legs; dragon: four legs and wings; spider: eight legs; demon: two
legs, two arms, wings and a tail), put your mesh at assets/<name>/model.glb, and work through:
tools/prep.sh <name> # set "prep" in creature.json first: real size, facing -Y
# place joints in assets/<name>/rig.json on the grid views; check with: tools/rig.sh <name> preview
tools/rig.sh <name> # build, skin, paint, reference frame
tools/dataset.sh <name> # 2,200 random poses, about 25 min
tools/train.sh <name> # the joint reader, about 20 min
tools/film.sh <name> <move> # after tools/make_prompt.py writes prompts/<name>/<move>.txt
tools/capture.sh <name> runs/<name>/videos/<move>/s1/h3max.mp4 <move>
The skills below walk an agent (or you) through each step, with the checks that tell a good capture from a bad one.
anymove is a Claude Code plugin and its own marketplace:
/plugin marketplace add Patch-gg/anymove
/plugin install anymove@anymove
| skill | for |
|---|---|
anymove |
the method, setup, which step comes next, costs |
anymove-rig |
mesh, facing and scale, placing joints in rig.json, building and painting the rig, framing |
anymove-film |
writing the prompt, generating clips on fal, checking them before capture |
anymove-reader |
rendering the dataset, training the reader, the exam in centimetres |
anymove-capture |
reading, fitting, the quality gates, fixing bad fits, showcase renders |
anymove-lora |
training and testing the MiniMax H3 LoRA on fal |
They're plain Agent Skills (skills/<name>/SKILL.md), so other agents can load them from that
folder too.
| creature | skeleton | reader error, front / side (px) | how well the views agree (1 = same footfalls) |
|---|---|---|---|
| wolf, 1.6 m | 30 bones, 36 joints | 6.9 / 3.8 | trot 0.94 · exam: 0.94 cm mean 3D joint error |
| dragon, 3 m | 42 bones, 50 joints, wings | 6.2 / 5.5 | take off 1.00, roar 0.93, swipe 0.85, walk 0.81 |
| giant spider, 2.4 m span | 28 bones, 40 joints, 8 legs | 2.9 / 3.6 | rear up 0.99, bite 0.96, pounce 0.89 |
| fire demon, 3.6 m | 38 bones, 46 joints, wings, tail | 8.4 / 9.4 | wing beat 0.99, slam 0.84, roar 0.73 (feet planted) |
Reader error is the held-out mean on the 720-row frame; the demon stands 412 px tall, so its 8.4 px is about 2% of its height. Every clip in the table is MiniMax H3 Max. The exam renders a made-up motion with known joints in the same studio and reads it back.
| a mesh, Tripo H3.1 with HD texture | $0.40 |
| a 5 s two-camera clip, MiniMax H3 Max | about $0.26 (H3: $0.30) |
| training renders, 2,200 poses x 2 views | about 25 min on one RTX 4090 |
| the joint reader, 20 epochs | 17-20 min, 12 GB |
| reading a clip / fitting it | 10 s / under a minute (20 s for the dragon's roar on a CPU) |
| the H3 LoRA, 1,200 steps on fal | about $12, about 2 h |
Prices on fal as of October 2026.