InfiniteDance

--- license: apache-2.0 --- # InfiniteDance — Data & Pretrained Weights This repo hosts the data and weights for [**InfiniteDance**](https://github.com/MotrixLab/InfiniteDance) (*Scalable 3D Dance Generation Towards in-the-wild Generalization*). **The directory layout here mirrors the GitHub repo** — every file's path on HF is exactly where it should live locally. You can drop this repo on top of a clone of the code repo without renaming anything. ## Contents (HF path → local path are identical) ``` models/checkpoints/dance_vqvae.pth # 462 MB → All_LargeDanceAR/models/checkpoints/ output/exp_m2d_infinitedance/best_model_stage2.pt # 2.3 GB → All_LargeDanceAR/output/exp_m2d_infinitedance/ # (full LLaMA-3.2-1B backbone is INSIDE this ckpt; # no download from Meta is required) InfiniteDanceData/ ├── DanceVQVAE/body_models/smpl/ # 40 MB → SMPL body models for visualization ├── dance/alldata_new_joint_vecs264/meta/ │ ├── Mean.npy # 264-d motion-feature mean │ └── Std.npy # 264-d motion-feature std ├── partition/ # train / val / test splits ├── styles/all_style_map.json # genre label map ├── Infinite_MotionTokens_512_vel_processed.tar.gz # 14 MB → motion tokens (training) ├── muq_features_test_infinitedance.tar.gz # 2.6 GB → MuQ features (test set) ├── musicfeature_55_allmusic_pure.tar.gz # 3.0 GB → 55-d music feats (BA metric) └── retrieval_s192_l384_style.tar.gz # 839 MB → top-K retrieval cache ``` ## Quick start ```bash # 1. clone the code repo git clone https://github.com/MotrixLab/InfiniteDance.git cd InfiniteDance # 2. download everything from HF directly on top of the clone — paths match, # so files land in the right place automatically pip install -U "huggingface_hub[cli]" huggingface-cli download huuuuuuuuu/InfiniteDance \ --repo-type model \ --local-dir . \ --local-dir-use-symlinks False # 3. extract the four tarballs in place cd InfiniteDanceData mkdir -p dance music/muq_features tar -xzf Infinite_MotionTokens_512_vel_processed.tar.gz -C dance/ tar -xzf retrieval_s192_l384_style.tar.gz -C dance/ tar -xzf musicfeature_55_allmusic_pure.tar.gz -C music/ tar -xzf muq_features_test_infinitedance.tar.gz -C music/muq_features/ cd .. # 4. run inference cd All_LargeDanceAR bash infer.sh ``` ## Expected layout after extraction ``` InfiniteDance/ ├── All_LargeDanceAR/ │ ├── models/checkpoints/dance_vqvae.pth │ ├── models/Llama3.2-1B/config.json # architecture only (shipped with code) │ └── output/exp_m2d_infinitedance/best_model_stage2.pt └── InfiniteDanceData/ ├── DanceVQVAE/body_models/smpl/ ├── dance/ │ ├── alldata_new_joint_vecs264/meta/{Mean,Std}.npy │ ├── Infinite_MotionTokens_512_vel_processed/ │ └── retrieval_s192_l384_style/ ├── music/ │ ├── muq_features/test_infinitedance/ │ └── musicfeature_55_allmusic_pure/ ├── partition/ └── styles/ ``` ## Notes - **LLaMA weights are bundled in the released checkpoint.** You do *not* need to download anything from Meta. The code repo ships only the architecture `config.json` so the model can be re-instantiated; the weights are loaded from `best_model_stage2.pt`. - The pretrained model uses a **per-frame MLP dance bridge** and an **n_bins=2 cadence MoE music bridge**, with anti-collapse strict decoding enabled by default (see `infer.sh`). - **Not yet released here:** the full 264-dim normalized motion features (`alldata_new_joint_vecs264/`, needed to train from scratch) and the SMPL-X GT joints (`ourData_smplx_22_smooth_new/`, needed to compute FID-k / FID-m / Div-k / Div-m). What you *can* reproduce today: inference on the released MuQ test set, inference on your own audio, the Beat-Align metric, and retrieval ablations. ## Citation ```bibtex @misc{li2026infinitedancescalable3ddance, title={InfiniteDance: Scalable 3D Dance Generation Towards in-the-wild Generalization}, author={Ronghui Li and Zhongyuan Hu and Li Siyao and Youliang Zhang and Haozhe Xie and Mingyuan Zhang and Jie Guo and Xiu Li and Ziwei Liu}, year={2026}, eprint={2603.13375}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2603.13375}, } ```