Abstract

We present HOI4D, a large-scale 4D egocentric dataset with rich annotations, to catalyze the research of category-level human-object interaction. HOI4D consists of 2.4M RGB-D egocentric video frames over 4000 sequences collected by 9 participants interacting with 800 different object instances from 16 categories over 610 different indoor rooms. Frame-wise annotations for panoptic segmentation, motion segmentation, 3D hand pose, category-level object pose and hand action have also been provided, together with reconstructed object meshes and scene point clouds. With HOI4D, we establish three benchmarking tasks to promote category-level HOI from 4D visual signals including semantic segmentation of 4D dynamic point cloud sequences, category-level object pose tracking, and egocentric action segmentation with diverse interaction targets. In-depth analysis shows HOI4D poses great challenges to existing methods and produces great research opportunities.

Overview of HOI4D

We construct a large-scale 4D egocentric dataset with rich annotation for category-level human-object interaction. Frame-wise annotations for action segmentation(a), motion segmentation(b), panoptic segmentation(d), 3D hand pose and category-level object pose(c) are provided, together with reconstructed object meshes(e) and scene point cloud.

Paper

          @InProceedings{Liu_2022_CVPR,
    author    = {Liu, Yunze and Liu, Yun and Jiang, Che and Lyu, Kangbo and Wan, Weikang and Shen, Hao and Liang, Boqiang and Fu, Zhoujie and Wang, He and Yi, Li},
    title     = {HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {21013-21022}
}

Data

News: We have released the test set with labels for HOI4D action segmentation and semantic segmentation tasks.

HOI4D_color. RGB_video.

HOI4D_depth. Depth_video.

HOI4D_CAD_models. CAD_model.

HOI4D_annotations. Annotations.

HOI4D_cameras. Camera_parameters.

HOI4D_Handpose. HOI4D_Handpose_1.

HOI4D_Instructions. Github.

HOI4D_Action_Segmentation. Github.

Dataset for HOI4D_Action_Segmentation(test set with labels). Dataset.

HOI4D_Semantic_Segmentation. Github.

Dataset for HOI4D_Semantic_Segmentation. Dataset.

Baidu Cloud download link. Baidu Cloud download link..

Contact

Send any comments or questions to Yunze Liu: liuyzchina@gmail.com. HOI4D is licensed under CC BY-NC 4.0.