AssemblyHands Toolkit

AssemblyHands Toolkit is a Python package that provides data loading, visualization, and evaluation tools for the AssemblyHands dataset. The dataset and results were originally reported in a CVPR 2023 paper, see below:

AssemblyHands: Towards Egocentric Activity Understanding via 3D Hand Pose Estimation
Takehiko Ohkawa, Kun He, Fadime Sener, Tomas Hodan, Luan Tran, and Cem Keskin
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023
[ paper ][ project page ][ arXiv ]

The dataset is also used in the challenge of the HANDS workshop in ICCV 2023.

Release notes

[Nov 09, 2023]: Add link to exocentric videos and visualizer (the 8 static RGB cameras used in automatic pose annotation).
[Aug 11, 2023]: Add video visualizer in visualization/visualizer.py
[Jun 26, 2023]: Update src/common/utils & upload skeleton.txt in the "annotations" folder
[Jun 26, 2023]: Fix a tar file of "nusar-2021_action_both_9075-c08b_9075_user_id_2021-02-12_101609.tar.gz" in the google drive
[May 24, 2023]: Open repository

Dependency

python >= 3.8.11
pytorch >= 1.11.0

Dataset preparation

• Download the assemblyhands dataset from google drive.
  [ego_images] [exo_videos] [annotations]

The number of images for each ego camera is not consistent because we only compressed valid images by excluding images without hands.

• Assume the assemblyhands data is stored in ${DATA_DIR}. The structure of ${DATA_DIR} is as follows

- ${DATA_DIR}
    - images
        - ego_images_rectified
            - split: {test, train, val}        
    - videos
        - exo_videos_rectified
            - nusar-2021_*
    - annotations
        - skeleton.txt
        - split: {demo, test, train, val}  
            - assemblyhands_${split}_ego_calib_v1-1.json
            - assemblyhands_${split}_ego_data_v1-1.json
            - assemblyhands_${split}_joint_3d_v1-1.json

• Link the data and annotations to this codebase.

mkdir -p data/assemblyhands
ln -s ${DATA_DIR}/images data/assemblyhands 
ln -s ${DATA_DIR}/videos data/assemblyhands
ln -s ${DATA_DIR}/annotations data/assemblyhands 

The files of invalid image list (invalid_${split}_${modality}.txt) will be generated when running the dataset.py first. With these files, image loading will be faster next time.

Annotation format

All annotations must be accessible in data/assemblyhands/annotations/.

• assemblyhands_${split}_${modality}_data.json: follows COCO format and is used for single-view image loading.
• assemblyhands_${split}_joint_3d.json: contains 3d keypoint anootations.
• assemblyhands_${split}_${modality}_calib.json: stores camera parameters with intrinsics and extrinsics.

Keypoint order: 0-20 Right, 21-41 Left

       (LEFT)              (RIGHT)
  37  33  29  25          4   8  12  16
  |   |   |   |           |   |   |   |
  38  34  30  26          5   9  13  17
  |   |   |   |  21    0  |   |   |   |
  39  35  31  27 |     |  6  10  14  18
  |   |   |   |  22    1  |   |   |   |
  40  36  32  28 |     |  7  11  15  19
   \  \   |   /  23    2  \   |   /  /
    \  \  |  /  /       \  \  |  /  /
     \  \ | /  24        3  \ | /  /
      \  -|-  /           \  -|-  /
          41                 20

Example of data loading and visualization

We have implemented a data loader in src/dataset/AssemblyHands-Ego based on the InterHand2.6M code.
You can run this visualization to check the loaded images and annotations.

python -m src.dataset.AssemblyHands-Ego.dataset

Visualization

Visualizing egocentric images

This is a script of the frame-wise visualizer loading a two-hand-pose json. Given the ground-truth or the predictions in world coordinates, you can visualize rectified egocentric images and export them to a video.

python -m visualization.visualizer

Visualizing exocentric videos

This is a script of the video-based visualizer for rectified exocentric videos. Note that released videos from the original Assembly101 dataset are neither recitified nor necessarily syncronized across all views, but ours are. You can find details regarding these videos in README.

python -m visualization.video_visualizer

Related projects

Single-to-Dual-View Adaptation for Egocentric 3D Hand Pose Estimation (CVPR 2024)

[S2DHand] proposes Single-to-Dual-view adaptation framework to adapt a traditional single-view hand pose estimator to arbitrary dual views. Pretrained single-view estimators are available.

ICCV 2023 HANDS challenge

This dataset is used in this competition: [AssemblyHands@ICCV23]. Test data and evaluation can be found in [HANDS2023-AssemblyHands]. Submitted technical reports are compiled into the following report.

[Benchmarks and Challenges in Pose Estimation for Egocentric Hand Interactions with Objects]:
Zicong Fan*, Takehiko Ohkawa*, Linlin Yang*, Nie Lin, Zhishan Zhou, Shihao Zhou, Jiajun Liang, Zhong Gao, Xuanyang Zhang, Xue Zhang, Fei Li, Liu Zheng, Feng Lu, Karim Abou Zeid, Bastian Leibe, Jeongwan On, Seungryul Baek, Aditya Prakash, Saurabh Gupta, Kun He, Yoichi Sato, Otmar Hilliges, Hyung Jin Chang, and Angela Yao (* equal contribution)

Citation & license

Please cite the following article if our code helps you.

@inproceedings{ohkawa:cvpr23,
    title     = {{AssemblyHands:} Towards Egocentric Activity Understanding via 3D Hand Pose Estimation},
    author    = {Takehiko Ohkawa and Kun He and Fadime Sener and Tomas Hodan and Luan Tran and Cem Keskin},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    pages     = {12999-13008},
    year      = {2023},
}

AssemblyHands Toolkit is CC-BY-NC 4.0 licensed, as found in the LICENSE file.

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