PACHO: Parallel Affordance and Contact Diffusion for Human-Object Interaction Synthesis

Text-driven 3D human-object interaction (HOI) synthesis requires precise spatiotemporal synchronization and stable physical contact between the human and object. Existing approaches struggle to capture the spatiotemporal co-evolution of localized human-contact and object-affordance regions, leading to loose physical coupling. To provide fine-grained spatial guidance, our key insight is to explicitly model the dynamic “handshake” between entities: the tightly coupled, time-varying human contact regions and functional object affordances. We propose $\text{PACHO}$, a novel diffusion framework that predicts object affordance and human contact maps in parallel to guide human–object motion denoising. Specifically, PACHO utilizes Dual Motion-Prior Learners to extract decoupled kinematic representations for both the human and the object. To bridge these independent streams, we introduce a Cross-Affordance Interaction module that performs adaptive, part-aware point-level contact-affordance reasoning to establish a contact-aware bidirectional physical coupling. During denoising, a joint motion-salience objective supervises the motion alongside the affordance and contact, encouraging local geometric consistency. Extensive experiments demonstrate that PACHO achieves state-of-the-art performance, with clear improvements in physical realism, interpenetration reduction and contact stability.