  This paper addresses a novel task of anticipating 3D human-object
interactions (HOIs). Most existing research on HOI synthesis lacks
comprehensive whole-body interactions with dynamic objects, e.g., often limited
to manipulating small or static objects. Our task is significantly more
challenging, as it requires modeling dynamic objects with various shapes,
capturing whole-body motion, and ensuring physically valid interactions. To
this end, we propose InterDiff, a framework comprising two key steps: (i)
interaction diffusion, where we leverage a diffusion model to encode the
distribution of future human-object interactions; (ii) interaction correction,
where we introduce a physics-informed predictor to correct denoised HOIs in a
diffusion step. Our key insight is to inject prior knowledge that the
interactions under reference with respect to contact points follow a simple
pattern and are easily predictable. Experiments on multiple human-object
interaction datasets demonstrate the effectiveness of our method for this task,
capable of producing realistic, vivid, and remarkably long-term 3D HOI
predictions.
