lxxiao/272-dim-BABEL-stream

🚀 Dataset Usage

To facilitate researchers, we provide the processed streaming 272-dim Motion Representation of BABEL dataset in this Hugging Face repo.

NOTE: We process the original BABEL dataset to support training of streaming motion generation. e.g. If there is a motion sequence A, annotated as (A1, A2, A3, A4) in BABEL dataset, each subsequence has text description: (A1t, A2t, A3t, A4t).

Then, our BABEL-stream is constructed as:

seq1: (A1, A2) --- seq1text: (A1t*A2t#A1length)

seq2: (A2, A3) --- seq2text: (A2t*A3t#A2length)

seq3: (A3, A4) --- seq3text: (A3t*A4t#A3length)

Here, * and # is separation symbol, A1_length means the number of frames of subsequence A1.

Motions are resampled into 30 FPS.

The dataset is organized as:

./
  ├── train_stream
      ├── seq1.npy
      ...
  ├── train_stream_text
      ├── seq1.txt
      ...
  ├── val_stream
      ├── seq1.npy
      ...
  ├── val_stream_text
      ├── seq1.txt
      ...

❗️❗️❗️ The processed data is solely for academic purposes. Make sure you read through the BABEL License.

📖 Paper & Project Page & Code

• —Arxiv Paper
• —Project Page
• —Code

🏃 Processing script

For more details of how to obtain the 272-dim motion representation, as well as other useful tools (e.g., Visualization and Conversion to BVH format), please refer to our GitHub repo.

🌹 Acknowledgement

This repository builds upon the following awesome datasets and projects:

• —BABEL

🤝🏼 Citation

If our project is helpful for your research, please consider citing :

@article{xiao2025motionstreamer,
      title={MotionStreamer: Streaming Motion Generation via Diffusion-based Autoregressive Model in Causal Latent Space},
      author={Xiao, Lixing and Lu, Shunlin and Pi, Huaijin and Fan, Ke and Pan, Liang and Zhou, Yueer and Feng, Ziyong and Zhou, Xiaowei and Peng, Sida and Wang, Jingbo},
      journal={arXiv preprint arXiv:2503.15451},
      year={2025}
    }