| --- |
| license: other |
| license_name: babel-amass-non-commercial |
| license_link: https://babel.is.tue.mpg.de/license.html |
| pretty_name: Triangular Resampling data release (BABEL 263D) |
| tags: |
| - motion-generation |
| - text-to-motion |
| - human-motion |
| - babel |
| - humanml3d |
| - diffusion |
| --- |
| |
| # Triangular Resampling: data release |
|
|
| Data for **Triangular Resampling for Long-Horizon Motion Generation** |
| ([arXiv:2609.34697](https://arxiv.org/abs/2609.34697)). |
| Code: [github.com/KunhangL/TriangularResampling](https://github.com/KunhangL/TriangularResampling). |
|
|
| This repository holds the BABEL data and the FloodDiffusion 2 (FD2) training metadata used |
| in the paper. These are the exact files of the paper's runs. HumanML3D is not included; it |
| comes with the [FloodDiffusion release](https://huggingface.co/ShandaAI/FloodDiffusionDownloads), |
| whose `HumanML3D.zip` is identical to the HumanML3D copy used in the paper. |
|
|
| | File | Extract into | Contents | SHA-256 | |
| | --- | --- | --- | --- | |
| | `BABEL.zip` | `raw_data/` | 263D BABEL features, annotations, FD1 VAE tokens, splits | `681fdd86ebc70d24bec18b1b83e04f0070a8ae8fc3cd3a33ee7f1cd7ba2dfdcf` | |
| | `manifests.zip` | `raw_data/` | FD2 training membership | `71b2b95ff2c072500562aa8ebe2b42d03dbbd4102b2d07f7e9c83e566d88cc32` | |
|
|
| ## Download |
|
|
| From a clone of the code repository (checks the SHA-256 of both archives): |
|
|
| ```bash |
| python -m common.download_data # both archives into raw_data/ |
| python -m methods.fd1.download_assets --with-dataset # FD1: deps, checkpoints, HumanML3D and BABEL |
| python -m methods.fd2.download_assets --with-deps --with-data # FD2: checkpoint, deps, BABEL, manifests |
| ``` |
|
|
| Or directly: |
|
|
| ```python |
| import zipfile |
| from huggingface_hub import hf_hub_download |
| |
| for name in ("BABEL.zip", "manifests.zip"): |
| path = hf_hub_download("KunhangLI/TriangularResampling", name) |
| zipfile.ZipFile(path).extractall("raw_data") |
| ``` |
|
|
| ## `BABEL.zip` |
|
|
| ``` |
| BABEL/ |
| ├── new_joint_vecs/<id>.npy 5,341 sequences, float32 (T, 263), 20 fps |
| ├── texts/<id>.txt 4,877 annotation files |
| ├── TOKENS_20251030_085836_vae_wan_z4/<id>.npy 5,341 VAE token files, float32 (ceil(T/4), 4) |
| ├── train.txt 3,985 ids (BABEL train split) |
| ├── val.txt 1,356 ids (BABEL validation split) |
| └── test_min.txt 10 ids (validation sequences used for rendering) |
| ``` |
|
|
| - **Features.** Every BABEL sequence (train and validation) in the 263-dimensional |
| HumanML3D motion representation at 20 fps, one file per BABEL sequence id, 25 to 5,272 |
| frames. Sequences are complete, not cut into segments. |
| - **Annotations.** HumanML3D-style text files with one line per labelled BABEL segment: |
| `caption#word/POS ...#start_sec#end_sec`. 464 sequences have no annotation; the code |
| skips them. |
| - **Tokens.** The latents of the official FloodDiffusion VAE (`vae_1d_z4_step=300000.ckpt`), |
| 4 frames per token, as read by the FD1 configs. They are the tokens of the paper's FD1 |
| runs, so FD1 can be trained without re-encoding. |
| - **Splits.** `train.txt` and `val.txt` follow BABEL's train / validation split. Training |
| uses the 3,640 annotated training sequences. The validation sequences serve as reference |
| motions for the paper's time-varying-text evaluation, whose frozen prompt manifest is |
| `common/evaluation/manifests/babel_text_streams_256.jsonl` in the code repository. |
|
|
| ## `manifests.zip` |
|
|
| ``` |
| manifests/ |
| └── humanml_babel.json FD2 training membership |
| ``` |
|
|
| - **`humanml_babel.json`** lists every HumanML3D and BABEL sample of the FD2 run, with |
| paths relative to the code repository root. It has 25,966 training samples, 4,384 |
| validation samples (the full HumanML3D test split) and 18 rendering samples. Its SHA-256, |
| `b27b204b48915114cd69ba016f25640e75ba809f94c496c27184cb6f63750bac`, is pinned in `methods/fd2/configs/humanml_babel_tr.yaml`. Running |
| `methods/fd2/prepare_data.py` from the repository root rebuilds the same file. Its |
| `problems` field lists two HumanML3D test features with non-finite values (`007975`, |
| `M007975`), which FD2 validation skips. |
| |
| ## License |
| |
| These files are derived from [BABEL](https://babel.is.tue.mpg.de/) (Punnakkal et al., |
| CVPR 2021), which builds on [AMASS](https://amass.is.tue.mpg.de/) (Mahmood et al., |
| ICCV 2019). Only extracted motion features, text annotations, model latents and id lists are |
| provided. No raw AMASS data (SMPL parameters or |
| meshes) is distributed. |
| |
| The data is for **non-commercial scientific research only**. By using it you agree to the |
| [BABEL license](https://babel.is.tue.mpg.de/license.html) and the |
| [AMASS license](https://amass.is.tue.mpg.de/license.html). If you need the raw motion data or a commercial license, register |
| with and contact the dataset owners. |
| |
| ## Citation |
| |
| ```bibtex |
| @article{li2026triangular, |
| title = {Triangular Resampling for Long-Horizon Motion Generation}, |
| author = {Li, Kunhang and Cai, Yiyi and Zhang, Xiangyue and Tu, Fangyuan and Wu, Yuhan and Wang, Zhixiang and Zhang, Kaipeng and Liu, Haiyang}, |
| journal = {arXiv preprint arXiv:2609.34697}, |
| year = {2026} |
| } |
| @inproceedings{punnakkal2021babel, |
| title = {{BABEL}: Bodies, Action and Behavior with English Labels}, |
| author = {Punnakkal, Abhinanda R. and Chandrasekaran, Arjun and Athanasiou, Nikos and Quiros-Ramirez, Alejandra and Black, Michael J.}, |
| booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, |
| year = {2021} |
| } |
| @inproceedings{mahmood2019amass, |
| title = {{AMASS}: Archive of Motion Capture as Surface Shapes}, |
| author = {Mahmood, Naureen and Ghorbani, Nima and Troje, Nikolaus F. and Pons-Moll, Gerard and Black, Michael J.}, |
| booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, |
| year = {2019} |
| } |
| ``` |
| |
