VPHO: Joint Visual-Physical Cue Learning and Aggregation for Hand-Object Pose Estimation
This addresses the challenge of physically plausible pose estimation for applications like augmented reality and human-computer interaction, representing a novel integration rather than an incremental step.
The paper tackled the problem of 3D hand-object pose estimation from a single RGB image by proposing a framework that jointly integrates visual and physical cues, resulting in significant improvements in pose accuracy and physical plausibility over state-of-the-art methods.
Estimating the 3D poses of hands and objects from a single RGB image is a fundamental yet challenging problem, with broad applications in augmented reality and human-computer interaction. Existing methods largely rely on visual cues alone, often producing results that violate physical constraints such as interpenetration or non-contact. Recent efforts to incorporate physics reasoning typically depend on post-optimization or non-differentiable physics engines, which compromise visual consistency and end-to-end trainability. To overcome these limitations, we propose a novel framework that jointly integrates visual and physical cues for hand-object pose estimation. This integration is achieved through two key ideas: 1) joint visual-physical cue learning: The model is trained to extract 2D visual cues and 3D physical cues, thereby enabling more comprehensive representation learning for hand-object interactions; 2) candidate pose aggregation: A novel refinement process that aggregates multiple diffusion-generated candidate poses by leveraging both visual and physical predictions, yielding a final estimate that is visually consistent and physically plausible. Extensive experiments demonstrate that our method significantly outperforms existing state-of-the-art approaches in both pose accuracy and physical plausibility.