LEMON: Learning 3D Human-Object Interaction Relation from 2D Images

• Main Authors: Yang, Yuhang, Zhai, Wei, Luo, Hongchen, Cao, Yang, Zha, Zheng-Jun
• Format: Conference Proceeding
• Language: English
• Subjects: Computer vision; Correlation; Geometry; human-object interaction; Solid modeling; Three-dimensional displays; Training; Uncertainty
• ISSN: 2575-7075
• DOI: 10.1109/CVPR52733.2024.01541

Learning 3D human-object interaction relation is piv-otal to embodied AI and interaction modeling. Most existing methods approach the goal by learning to predict isolated interaction elements, e.g., human contact, object affordance, and human-object spatial relation, primarily from the perspective of either the human or the object. Which underexploit certain correlations between the interaction counterparts (human and object), and struggle to address the uncertainty in interactions. Actually, objects' functionalities potentially affect humans' interaction intentions, which reveals what the interaction is. Mean-while, the interacting humans and objects exhibit matching geometric structures, which presents how to interact. In light of this, we propose harnessing these inherent correlations between interaction counterparts to mitigate the uncertainty and jointly anticipate the above interaction el-ements in 3D space. To achieve this, we present LEMON (LEarning 3D huMan-Object iNteraction relation), a unified model that mines interaction intentions of the counter-parts and employs curvatures to guide the extraction of ge-ometric correlations, combining them to anticipate the interaction elements. Besides, the 3D Interaction Relation dataset (3DIR) is collected to serve as the test bed for training and evaluation. Extensive experiments demonstrate the superiority of LEMON over methods estimating each element in isolation. The code and dataset are available at https://yyvhang.github.io/LEMON.