KunhangLI/TriangularResampling

Triangular Resampling: data release

Data for Triangular Resampling for Long-Horizon Motion Generation (arXiv:2609.34697). Code: 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, 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):

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:

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 (Punnakkal et al., CVPR 2021), which builds on AMASS (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 and the AMASS license. If you need the raw motion data or a commercial license, register with and contact the dataset owners.

Citation

@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}
}