⏳ This skill is pending AI review.
Scores will appear once the review pipeline completes.
anymove
Motion capture for any creature from one AI video, with the anymove repo. A video model films a rigged 3D creature from two cameras in one frame, a DINOv2 joint reader trained on that creature finds every joint in both views, and the rig is fitted to them and exported as a GLB. Use when someone wants to animate a non-human creature (dragon, spider, wolf, monster) from a text prompt or an AI video, asks how anymove works, or needs to know which step comes next; it routes to anymove-rig, anymove-film, anymove-reader, anymove-capture, anymove-pack and anymove-lora.
Choose how to use this skill
You do not need every option. Choose the path your AI client supports. The stable page stays the same; versioned files are immutable.
1. Native installer
This listing has no registered native installer command. Use the complete package or source fallback below, depending on what your client supports.
Do not guess an installer command or replace an existing version without reviewing the diff.
2. Complete package recommended
Download the ZIP when available. It includes SKILL.md plus the references, security notes and version metadata.
No complete ProSkills package is published for this listing yet.3. Prompt-only
Copy the prompt above when the agent can read the stable page or when you want to adopt the workflow without installing a skill.
Need only the instruction file?
Download SKILL.md only if your client requires a single file. The complete ZIP is safer for a full installation because it preserves the references and release context.
No path installs or executes anything by itself. Your agent still needs access to the project files. Before updating, compare the installed version and review the diff.
// RATINGS
Not yet listed on ClawHub or SkillsMP
// README
@patch_gg · dragon, giant spider, fire demon, wolf · about $3 for a usable move, all in.
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.
Just want the creatures? Nine are ready in Patch's Assets, with 4 to 7 moves each: T-rex, knight, horse, centaur, woman, ogre, ape, mech and scorpion.
before you start.
- You need: an NVIDIA card (12 GB to train a reader, 24 GB is safer), Blender 5.x, Python 3.11 and a fal key. Without the card it doesn't run, and there's no cloud version.
- It costs about $3 for a usable move, all in, and about $17 and a couple of hours for a new creature with about 5 moves. The numbers are below.
- Most clips don't make it. A move took about 7 clips on average, and 58% of captured clips passed the checks.
what it's good at, and what it isn't.
Good at: big single gestures on any body plan: roars, slams, rearing up, chest beats, stomps, taking off. Walks and runs work on two and four legs. You get a real animation on a real skeleton, in a file any engine plays.
Not good at:
- Eight-legged walking. The two views disagree about which legs are up (0.36-0.49). Scorpions and spiders can rear, bite and strike, but they don't walk.
- Lying down. The reader learns from random poses inside the joint limits, which don't include lying on a side.
- Bird wings. The eagle's wings don't fit (15-30 px off), and it was left out of the nine. Bat-like wings do fit, on the dragon and the demon.
- Tail swipes. The two views disagree about where the tail tip is. The T-rex's was dropped after six tries.
- Fine foot contact. Feet slide 1 to 2 cm a step and nothing pins them yet. That's fine for most uses, and visible up close.
- Fast runs. Runs look like fast walks in places: the video model doesn't lower the body on a run.
- Moving an animation to another creature. An animation only fits the creature it was made for, and a new model needs a new reader.
- Light files. Generated creatures are 55,000 to 60,000 triangles with 4K textures, which is heavy for a browser game, and there are no lighter versions yet.
how it works.
| 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 |
Then the best takes become one file, with seamless loops and root motion (docs/PACKS.md). docs/PIPELINE.md has the file-by-file version.
quick start.
You need an NVIDIA GPU with CUDA (12 GB to train a reader, 24 GB is safer; 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
On Windows, use Git Bash, and the same commands with the venv's other folder.
Every tools/*.sh finds the venv and handles Blender's paths and Python's line endings. Blender usually isn't on your
PATH, so point at it:
// HOW IT'S BUILT
// FILES
2 files shipped with this skill. Inline text is the catalog copy. The upstream link is the source repository.
skills/anymove/SKILL.md5.9 KB6954e2413a51upstream ↗
--- name: anymove description: Motion capture for any creature from one AI video, with the anymove repo. A video model films a rigged 3D creature from two cameras in one frame, a DINOv2 joint reader trained on that creature finds every joint in both views, and the rig is fitted to them and exported as a GLB. Use when someone wants to animate a non-human creature (dragon, spider, wolf, monster) from a text prompt or an AI video, asks how anymove works, or needs to know which step comes next; it routes to anymove-rig, anymove-film, anymove-reader, anymove-capture, anymove-pack and anymove-lora. license: Apache-2.0 compatibility: Needs an NVIDIA GPU with CUDA (12 GB to train a reader, 24 GB is safer), Blender 5.x, Python 3.11, and a fal.ai key for video generation. Runs on Linux and on Windows in Git Bash. --- # anymove Animate any rigged creature from one AI video. The trick is the frame: one image holds two orthographic views of the creature at the same scale, front on the left and side on the right, sharing the vertical axis. A joint sits on the same pixel row in both halves, so the front view gives left-right and height, the side view gives front-back and height, and together they pin the joint in 3D. The method is @CanItCode's; the LoRA step is @Graalitoo's idea. ## The pipeline | step | once per | what happens | skill | |---|---|---|---| | 1. mesh | creature | a textured 3D model (Tripo H3.1 from one image, or your own GLB), facing -Y, real size | anymove-rig | | 2. skeleton | creature | joints placed in `assets/<c>/rig.json`, rig built and skinned in Blender | anymove-rig | | 3. limb colours | creature | each limb painted its own colour so left and right never swap | anymove-rig | | 4. two cameras | move | the painted reference frame plus a strict prompt go to MiniMax H3 Max on fal | anymove-film | | 5. teach a reader | creature | 2,200 random poses rendered with known joints; DINOv2 learns this creature | anymove-reader | | 6. read every joint | clip | the reader finds every joint in both views, every frame | anymove-capture | | 7. two views, 3D | clip | shared heights tie the views; the pair gives each joint in 3D | anymove-capture | | 8. the rig follows | clip | forward kinematics in torch fitted to both views; GLB out | anymove-capture | | 9. the pack | creature | score the takes, keep the best, cut loops and root motion; one GLB with every move | anymove-pack | | optional | creature | a MiniMax H3 LoRA that keeps the two-camera layout and colours more reliably | anymove-lora | ## Before anything 0. **Check it's the right tool.** If the person only wants a creature, look in Patch's Assets first: nine are ready. anymove suits big single gestures, and doesn't do eight-legged walking, lying down, bird wings or tail swipes (see the README). Check there's an NVIDIA card before anything else; without one, stop and say so. 1. Work in an anymove checkout. If the working directory has no `tools/capture.sh`, clone it: `git clone https://github.com/Patch-gg/anymove && cd anymove`. 2. Set up once: `python3.11 -m venv .venv`, then `.venv/bin/pip install torch==2.11.0 --index-url https://download.pytorch.org/whl/cu128` and `.venv/bin/pip install -r requirements.txt`. Blender 5.x must be on PATH, or set `BLENDER=/path/to/blender`. **On Windows,** run everything in Git Bash, use `py -3.11 -m venv .venv` and `.venv/Scripts/` where these say `.venv/bin/`, and set `BLENDER`. `tools/*.sh` handle the rest. If you call Blender yourself rather than through them, give it absolute paths: on Windows it reads a relative output path from the drive root. 3. For video generation and LoRA training, put `FAL_KEY=...` in `.env` (git ignores it) or export it. Never print it. 4. Every command takes a creature name; its config is `assets/<c>/creature.json` and every output lands in `runs/<c>/` (reference frame, dataset, `reader.pt`, videos, clips). The scripts set `MOCAP_CREATURE` for you; when you run a `python -m mocap...` module by hand, set `MOCAP_CREATURE=assets/<c>` first, or the wolf's layout applies. ## Which step next - No `assets/<c>/model.glb`: make or fetch the mesh. Examples: `tools/fetch_examples.sh <c>` (wolf, dragon, spider, demon). New creature: anymove-rig. - No `assets/<c>/rigged/rigged.blend` or `runs/<c>/ref/ref.png`: `tools/prep.sh <c>` then `tools/rig.sh <c>` (anymove-rig). - No `runs/<c>/reader.pt`: anymove-reader (about 45 min on one RTX 4090), or `tools/fetch_examples.sh <c> --readers`. - No clip for the move: anymove-film (about $0.26 a 5 s clip, and about 7 clips for a usable move). - A clip: anymove-capture, `tools/capture.sh <c> <video.mp4> <name>`. - Several good takes: anymove-pack, `tools/pack.sh <c>`. - Clips keep swapping limb colours or the halves drift apart: anymove-lora. ## Try it without a GPU The repo carries the dragon's roar as read by its reader. Fit it on the CPU in about a minute and a half: ```bash 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 ``` It prints the paw agreement (about 0.93) and the reprojection error per view; `motion.json` holds the keyframes. ## Costs and time (October 2026, one RTX 4090) **Quote the all-in numbers, not the price of one clip:** about $3 for a usable move, and about $17 and a couple of hours for a new creature with about 5 moves (about $11 of AI services on the person's own card). A move took about 7 clips on average ($1.80), and 58% of captured clips passed the checks. - Tripo H3.1 mesh with HD texture: $0.40. MiniMax H3 Max clip: about $0.26 for 5 s (H3: $0.30). - Dataset and reader training: about 75 min on one RTX 4090. Reading a clip: about 12 s. Fitting: about 45 s. GLB and renders: about 3 min. - A LoRA: $0.01 a training step, so 1,200 steps cost about $12 and take about 2 h on fal. Say what a step will cost before calling fal, and confirm with the person before spending on a batch.README.mdupstream ↗
Contents are not inlined in the catalog. Open the upstream file.
Plain-text copy, no login: /skill-bundles/patch-gg-anymove-skills-anymove.md