Reactffusion: Physical Contact-guided Diffusion Model for Reaction Generation

摘要

Current reaction generation studies often assume the homogeneity of all reactor body joints in the end-to-end motion generation while neglecting the physical contact information, resulting in evident joint mismatches in both temporal and spatial dimensions. In this paper, we introduce our method, Reactffusion, which addresses the reaction joint mismatch issue by explicitly leveraging the guidance from the actor-reactor physical contacts. At the mathematical modeling level, we reformulate the contact-guided reaction generation as a multi-task problem, divided into two sub-problems: contact information learning and reaction generation with physical constraints. Specifically, given the actor motion sequence, we first introduce a Contact Prediction Module (CPM), which adopts a spatial and temporal attentive mechanism to forecast the contact map, indicating the timing and the location of the potential joint collisions. Then, we employ the contact map as an explicit guide to rectify the sampling distribution in the denoising process of the proposed diffusion network. The comprehensive evaluations prove our method can achieve state-of-the-art performance compared with other reaction generation methods across multiple public benchmarks. Furthermore, the contact map predicted by the CPM can also effectively boost other baselines as an extra plug-in.