3D hand-object pose estimation dataset

Released in: ArtiBoost: Boosting Articulated 3D Hand-Object Pose Estimation via Online Exploration and Synthesis



Estimating the articulated 3D hand-object pose from a single RGB image is a highly ambiguous and challenging problem, requiring large-scale datasets that contain diverse hand poses, object types, and camera viewpoints. Most real-world datasets lack these diversities. In contrast, data synthesis can easily ensure those diversities separately. However, constructing both valid and diverse hand-object interactions and efficiently learning from the vast synthetic data is still challenging. To address the above issues, ArtiBoost uses a lightweight online data enhancement method. ArtiBoost can cover diverse hand-object poses and camera viewpoints through sampling in a Composited hand-object Configuration and Viewpoint space (CCV-space) and can adaptively enrich the current hard-discernable items by loss-feedback and sample re-weighting. ArtiBoost alternatively performs data exploration and synthesis within a learning pipeline, and
those synthetic data are blended into real-world source data for training. In the paper, ArtiBoost is applied on a simple learning baseline network and leads to a performance boost on several hand-object benchmarks.


Images in dataset


Year Released

Key Links & Stats


@inproceedings{li2021artiboost, title={{ArtiBoost}: Boosting Articulated 3D Hand-Object Pose Estimation via Online Exploration and Synthesis}, author={Li, Kailin and Yang, Lixin and Zhan, Xinyu and Lv, Jun and Xu, Wenqiang and Li, Jiefeng and Lu, Cewu}, booktitle={arXiv preprint arXiv:2109.05488}, year={2021} }



  1. 3D Asset


  1. Digital Human
  2. Home/Office

ML Task

  1. Object Detection
  2. Semantic Segmentation
  3. Instance Segmentation
  4. Human Pose Estimation
  5. Activity Recognition
  6. Depth Estimation
  7. Object Recognition

Related organizations

Shanghai Jiao Tong University

Shanghai Qi Zhi Institute