✨ [CVPR 2024] TACO: Benchmarking Generalizable Bimanual Tool-ACtion-Object Understanding ✨

[CVPR 2024] Official repository of "TACO: Benchmarking Generalizable Bimanual Tool-ACtion-Object Understanding".

📃Paper | 🏠Project | 🎥Video | 📁Dataset (pre-released version) | 📁Dataset

Authors

Yun Liu, Haolin Yang, Xu Si, Ling Liu, Zipeng Li, Yuxiang Zhang, Yebin Liu, Li Yi

Data Instruction

Pre-released Version

The pre-released version contains 244 high-quality motion sequences spanning 137 <tool, action, object> triplets. Please refer to the "Data Visualization" section for data usage.

We back up the data at BaiduNetDisk. Some of the files are split due to file size limitations. To get the original zip files, please use the following commands:

cat Allocentric_RGB_Videos_split.* > Allocentric_RGB_Videos.zip
cat Egocentric_Depth_Videos_split.* > Egocentric_Depth_Videos.zip

Dataset contents:

• 244 high-quality motions sequences spanning 137 <tool, action, object> triplets
• 206 High-resolution object models (10K~100K faces per object mesh)
• Hand-object pose and mesh annotations
• Egocentric RGB-D videos
• 8 allocentric RGB videos

Whole Dataset (Version 1)

The whole dataset (version 1) contains 2317 motion sequences. Please refer to the "Data Visualization" section for data usage.

This link is a backup of this dataset.

Dataset contents:

• 2317 motions sequences spanning 151 <tool, action, object> triplets
• 206 High-resolution object models (10K~100K faces per object mesh)
• Hand-object pose and mesh annotations
• Egocentric RGB-D videos
• 12 allocentric RGB videos
• Camera parameters
• Automatic Hand-object 2D segmentations

If you have questions about the dataset, please contact [email protected].

Data Organization

The files of the dataset are organized as follows:

|-- Allocentric_RGB_Videos
  |-- <triplet_1>
    |-- <sequence_1>
      |-- 22070938.mp4
      |-- 22139905.mp4
      ...
    |-- <sequence_2>
    ...
  |-- <triplet_2>
  ...
|-- Egocentric_Depth_Videos
  |-- <triplet_1>
    |-- <sequence_1>
      egocentric_depth.avi
    |-- <sequence_2>
    ...
  |-- <triplet_2>
  ...
|-- Egocentric_RGB_Videos
  |-- <triplet_1>
    |-- <sequence_1>
      color.mp4
    |-- <sequence_2>
    ...
  |-- <triplet_2>
  ...
|-- Hand_Poses
  |-- <triplet_1>
    |-- <sequence_1>
      left_hand_shape.pkl
      left_hand.pkl
      right_hand_shape.pkl
      right_hand.pkl
    |-- <sequence_2>
    ...
  |-- <triplet_2>
  ...
|-- Object_Poses
  |-- <triplet_1>
    |-- <sequence_1>
      target_<target_name>.npy
      tool_<tool_name>.npy
    |-- <sequence_2>
    ...
  |-- <triplet_2>
  ...
|-- Object_Models
  |-- 001_cm.obj
  ...
  |-- 218_cm.obj

Data Visualization

[1] Environment Setup:

Our code is tested on Ubuntu 20.04 with NVIDIA GeForce RTX 3090. The driver version is 535.146.02. The CUDA version is 12.2.

Please install the environment using the following commands:

conda create -n taco python=3.9
conda activate taco
<install PyTorch >= 1.7.1, we use PyTorch 1.11.0>
<install PyTorch3D >= 0.6.1, we use PyTorch3D 0.7.2>
pip install -r requirements.txt

[2] Download MANO models, and put MANO_LEFT.pkl and MANO_RIGHT.pkl in the folder dataset_utils/manopth/mano/models.

[3] Visualize Hand-Object Poses:

cd dataset_utils
python visualization.py --dataset_root <dataset root directory> --object_model_root <object model root directory> --triplet <triplet name> --sequence_name <sequence name> --save_path <path to save the visualization result> --device <device for the rendering process>

For example, if you select the following data sequence:

python visualization.py --dataset_root <dataset root directory> --object_model_root <object model root directory> --triplet "(stir, spoon, bowl)" --sequence_name "20231105_019" --save_path "./example.gif" --device "cuda:0"

You can obtain the following visualization result:

[4] Parse Egocentric Depth Videos:

Please use the following command for each video:

ffmpeg -i <egocentric depth video path> -f image2 -start_number 0 -vf fps=fps=30 -qscale:v 2 <egocentric depth image save path>

For example:

mkdir ./decode
ffmpeg -i ./egocentric_depth.avi -f image2 -start_number 0 -vf fps=fps=30 -qscale:v 2 ./decode/%05d.png

Each depth image is a 1920x1080 uint16 array. The depth scale is 1000 (i.e. depth values are stored in millimeters).

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{liu2024taco,
  title={Taco: Benchmarking generalizable bimanual tool-action-object understanding},
  author={Liu, Yun and Yang, Haolin and Si, Xu and Liu, Ling and Li, Zipeng and Zhang, Yuxiang and Liu, Yebin and Yi, Li},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={21740--21751},
  year={2024}
}

License

This work is licensed under a CC BY 4.0 license.

Email

If you have any questions, please contact [email protected].