Hand-object reconstruction via interaction-aware graph attention mechanism
This work addresses the need for advanced vision computing in hand-object pose estimation, but it appears incremental as it builds on existing graph neural network methods by refining edge connections.
The paper tackled the problem of reconstructing hand and object poses by addressing the challenge of understanding hand-object interactions like contact and physical plausibility, proposing an interaction-aware graph attention mechanism that improved physical plausibility in experiments.
Estimating the poses of both a hand and an object has become an important area of research due to the growing need for advanced vision computing. The primary challenge involves understanding and reconstructing how hands and objects interact, such as contact and physical plausibility. Existing approaches often adopt a graph neural network to incorporate spatial information of hand and object meshes. However, these approaches have not fully exploited the potential of graphs without modification of edges within and between hand- and object-graphs. We propose a graph-based refinement method that incorporates an interaction-aware graph-attention mechanism to account for hand-object interactions. Using edges, we establish connections among closely correlated nodes, both within individual graphs and across different graphs. Experiments demonstrate the effectiveness of our proposed method with notable improvements in the realm of physical plausibility.