Datasets:

L-yiheng
/

OmniHuMo

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 82, in _split_generators
                  raise ValueError(
              ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 66, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                         ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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OmniHuMo

Overview

OmniHuMo comprises over 3.2 million high-quality motion-capture sequences — sourced entirely from internet videos — totaling more than 5,000 hours.

Get Started

# Ensure git-lfs is installed (https://git-lfs.com)
git lfs install
# When prompted for a password, use an access token with write permissions.
# Generate one in your settings: https://huggingface.co/settings/tokens
git clone https://huggingface.co/datasets/L-yiheng/OmniHuMo
# To clone without large files (only their pointers)
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/L-yiheng/OmniHuMo

Dataset Processing

Folder Hierarchy

.
|-- README.md
|-- assets
|-- audio.tar.gz.part_aa
|-- audio.tar.gz.part_ab
|-- audio.tar.gz.part_ac
|-- audio.tar.gz.part_ad
|-- audio.tar.gz.part_ae
|-- audio.tar.gz.part_af
|-- audio.tar.gz.part_ag
|-- audio.tar.gz.part_ah
|-- audio.tar.gz.part_ai
|-- audio.tar.gz.part_aj
|-- audio.tar.gz.part_ak
|-- audio.tar.gz.part_al
|-- audio.tar.gz.part_am
|-- audio.tar.gz.part_an
|-- audio.tar.gz.part_ao
|-- audio.tar.gz.part_ap
|-- audio.tar.gz.part_aq
|-- audio.tar.gz.part_ar
|-- audio.tar.gz.part_as
|-- audio.tar.gz.part_at
|-- audio.tar.gz.part_au
|-- audio.tar.gz.part_av
|-- audio.tar.gz.part_aw
|-- audio.tar.gz.part_ax
|-- audio.tar.gz.part_ay
|-- audio.tar.gz.part_az
|-- audio.tar.gz.part_ba
|-- audio.tar.gz.part_bb
|-- audio.tar.gz.part_bc
|-- audio.tar.gz.part_bd
|-- audio.tar.gz.part_be
|-- audio.tar.gz.part_bf
|-- audio.tar.gz.part_bg
|-- audio.tar.gz.part_bh
|-- audio_feat.tar.gz.part_aa
|-- audio_feat.tar.gz.part_ab
|-- audio_feat.tar.gz.part_ac
|-- audio_feat.tar.gz.part_ad
|-- audio_feat.tar.gz.part_ae
|-- audio_feat.tar.gz.part_af
|-- audio_feat.tar.gz.part_ag
|-- audio_feat.tar.gz.part_ah
|-- omnihumo_v0.tar.gz.part_aa
|-- omnihumo_v0.tar.gz.part_ab
|-- omnihumo_v0.tar.gz.part_ac
|-- omnihumo_v0.tar.gz.part_ad
|-- omnihumo_v0.tar.gz.part_ae
|-- omnihumo_v0.tar.gz.part_af
|-- process_code
|-- split.tar.gz
`-- upload.py

Decompression

cat ./audio_feat.tar.gz.part_* | pigz -d -p 64 | tar -xvf -
cat ./audio.tar.gz.part_* | pigz -d -p 64 | tar -xvf -
cat ./omnihumo_v0.tar.gz.part_* | pigz -d -p 64 | tar -xvf -
tar -xzf split.tar.gz

License and Citation

All the data and code within this repo are under CC BY-NC-SA 4.0. Please consider citing our project if it helps your research.

@misc{li2026anymoscalinganymodalityconditional,
      title={AnyMo: Scaling Any-Modality Conditional Motion Generation with Masked Modeling},
      author={Yiheng Li and Zhuo Li and Ruibing Hou and Yingjie Chen and Hong Chang and Hao Liu and Shiguang Shan},
      year={2026},
      eprint={2605.29488},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2605.29488},
}

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Paper for L-yiheng/OmniHuMo

AnyMo: Scaling Any-Modality Conditional Motion Generation with Masked Modeling

Paper • 2605.29488 • Published 6 days ago