patch-gg/anymove

选用指南

AI 根据 README 整理 · 2026/10/27 22:12:03

使用方式

通过命令行工具和 Agent Skills 使用，支持从 AI 视频生成动画，也可用于自定义生物。

查看 README 依据

clip; this fits the rig to them (about 20 seconds) and prints how well the two views agree: ```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 ``` The whole capture, from the AI clip to a GLB, with an example creature's mesh, trained reader and clip from the [release](https://github.com/Patch-gg/anymove/releases/tag/examples-v1) (13 MB mesh, 355 MB reader, 4 MB clip): ```bash tools/fetch_examples.sh dragon --readers --clips

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. ## agent skills. anymove is a [Claude Code](https://code.claude.com) plugin and its own marketplace: ``` /plugin marketplace add Patch-gg/anymove

部署要求

需要 NVIDIA GPU 且显存至少 24 GB 用于训练关节读取器，Python 3.11，Blender 5.x 在 PATH 中，以及 fal API 密钥。

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| <img src="docs/steps/step-8.png" width="320" alt="The rig as an x-ray, its bones landed on the 3D points, stray points in grey."> | **8 · the rig follows.**<br>The rig bends its bones onto the points, ignores the strays, and exports for any engine.<br>_{glb, 24 fps, no hand edits · under a minute a clip} | [docs/PIPELINE.md](docs/PIPELINE.md) has the file-by-file version. ## quick start. You need an NVIDIA GPU with CUDA (24 GB to train a reader; reading and fitting need much less), Python 3.11,

[Blender](https://www.blender.org) 5.x on your `PATH` (or `BLENDER=/path/to/blender`), and a [fal](https://fal.ai) key to make new clips. ```bash 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

已知限制

八足行走不适用，躺下动作不适用，骨架需手动放置，视频模型可能漂移，LoRA 数据为 AI 生成，地面接触仅支持特定爪名，毛皮和布料需要额外骨骼。

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| 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. ## limits. - **Eight-legged walking doesn't fit.** The two views disagree about which legs are up (0.36-0.49). Big single gestures work best: rearing, roaring, slamming, taking off, pouncing.

- **Lying down doesn't fit.** The reader learns from random poses inside the joint limits, which don't include lying on a side; the wolf's death and lie-down clips failed. - **Skeletons are placed by hand** on grid views (`tools/prep.sh`, `tools/rig.sh preview`). UniRig could do it; its install pulls prebuilt flash-attn wheels. - **Video models drift.** Plain MiniMax H3 swapped the limb colours on 2 of 5 wolf clips; with the LoRA, 0 of 5 (one seed per move: a first read, not a benchmark). Seedance 2.5 played the views out of step on a trot.

- **The LoRA's data is AI-derived.** Its training clips re-render motions solved from AI clips, so the halves are in sync by construction but the motion itself is AI-made. Real dog mocap exists (AI4Animation) under a non-commercial licence. - **Ground contact is wolf- and dragon-shaped.** The solver's ground term knows paw names `fpaw`, `ftoe`, `hpaw`, `htoe`; other creatures fit without it. - **Fur and cloth** need extra bones: springs (`apply_motion.py --springs`) or tracked tips (the wolf's tracked fur read 7-28° swings from the video). ## licence.