Visual Relation Diffusion for Human-Object Interaction Detection

Human-object interaction (HOI) detection relies on finegrained visual understanding to distinguish complex relationships between humans and objects. While recent generative diffusion models have demonstrated remarkable capability in learning detailed visual concepts through pixellevel generation, their potential for interaction-level relationship modeling remains largely unexplored. To bridge this gap, we propose a Visual Relation Diffusion model (VRDiff), which introduces dense visual relation conditions to guide interaction understanding. Specifically, we encode interaction-aware condition representations that capture both spatial responsiveness and contextual semantics of human-object pairs, conditioning the diffusion process purely on visual features rather than text-based inputs. Furthermore, we refine these relation representations through generative feedback from the diffusion model, enhancing HOI detection without requiring image synthesis. Extensive experiments on the HICO-DET benchmark demonstrate that VRDiff achieves competitive results under both standard and zero-shot HOI detection settings.