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We propose CG-HOI, the first method to address the task of generating dynamic3D human-object interactions (HOIs) from text. We model the motion of bothhuman and object in an interdependent fashion, as semantically rich humanmotion rarely happens in isolation without any interactions. Our key insight isthat explicitly modeling contact between the human body surface and objectgeometry can be used as strong proxy guidance, both during training andinference. Using this guidance to bridge human and object motion enablesgenerating more realistic and physically plausible interaction sequences, wherethe human body and corresponding object move in a coherent manner. Our methodfirst learns to model human motion, object motion, and contact in a jointdiffusion process, inter-correlated through cross-attention. We then leveragethis learned contact for guidance during inference synthesis of realistic,coherent HOIs. Extensive evaluation shows that our joint contact-basedhuman-object interaction approach generates realistic and physically plausiblesequences, and we show two applications highlighting the capabilities of ourmethod. Conditioned on a given object trajectory, we can generate thecorresponding human motion without re-training, demonstrating stronghuman-object interdependency learning. Our approach is also flexible, and canbe applied to static real-world 3D scene scans.

CG-HOI: Contact-Guided 3D Human-Object Interaction Generation