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Guide to HumanData and View tools

What is HumanData?

HumanData is designed to provide a unified format for SMPL/SMPLX datasets to support joint training and evaluation.

The project is maintained in MMHuman3D. See detailed info for data structure and sample usage.

If you want to create your own humandata file, please refer to the sample below and maintain the similiar structure. Basically it is a big dictionary with some lists or dicts of lists, any dict with the correct structure works (Not necesscarily in HumanData class).

Sample Visualization Script

We provide a simple script to check the annotation and visualize the results. The script will read the annotation from HumanData and render it on the corresponding image using pyrender.

Download

Download sample here: Hugging Face

Extract

Follow the file structure as in main page. Extract to data folder, the structure should look like this:

├── data
│   ├── annot
│   │    └── hd_10sample.npz # sample annotation
│   └── img # original data files
│        └── egocentric_color

Environment

Basically you can directly install pyrender and trimesh to your environment, I tested many platforms without finding confilcts. CPU version of pytorch is also supported.

conda create -n hd_vis python=3.9
conda activate hd_vis
conda install torch torchvision torchaudio cudatoolkit=11.3 -c pytorch
pip install pyrender trimesh numpy opencv-python tqdm smplx

Visualization

Fixed command to for demo sample.

python humandata_prep/check.py \ 
    --hd_path data/annot/hd_10sample.npz \ 
    --image_folder data/img \ 
    --output_folder data/vis_output \ 
    --body_model_path human_models/human_model_files
  • Rendered image will be saved in the output folder.

Important Points: when visualizing other humandata files

This section is for those who want to debug or create their own humandata files.

  • Check flat_hand_mean if is correctly set, for humandata, it shoule be specified in hd['misc']['flat_hand_mean'] or by default False
  • Check gender
  • For some specific datasets, they might provide mesh vertices instead of SMPL/SMPLX parameters, we suggest to fit the mesh to parameters for every instance to maintain the consistency of the visualization. Some of those datasets are:
    • Arctic: They provide vtemplate instead of betas
    • EHF: They provide mesh files
  • Standalone SMPLX parameters fitting script