ContA-HOI: Towards Physically Plausible Human-Object Interaction Generation via Contact-Aware Modeling

Zhe Li, Jiakun Li, Mingqi Gao, Jinyu Yang, Wei Wang, Feng Zheng

Keywords: Human-object interaction generation

Abstract:

In human-object interaction (HOI), physical contact between the body and objects is a primary determinant of realism and plausibility.
Prior HOI methods typically encode relations via global joint-to-centroid or joint-to-boundary.
Such strategies neglect contact anchors that are essential for defining joint-to-contact relations-where and how HOI occurs, thereby implicitly reducing the problem to nearest-distance optimization.
Without explicit contact anchors and joint-to-contact dynamics, previous models drift toward artifacts: human-object penetration or unnatural object floating.
We argue that modeling contact relationships by contact anchors is important for generating realistic HOIs, as it directly captures where and how humans physically interact with objects rather than merely minimizing spatial proximity.
To address these limitations, we propose Contact-Aware HOI (ContA-HOI), a progressive framework that decomposes HOI generation into three synergistic stages: discovering where contact occurs, modeling how contact evolves, and guiding generation with contact constraints.
First, a Contact Affordance Predictor (CAP) addresses the "where" by predicting precise object-surface contact anchors from text, human pose, and object geometry.
Second, these anchors seed a Contact Relation Field (CRF) that captures "how" by modeling spatiotemporal dynamics of joint-to-contact relations throughout the interaction.
Finally, a Contact Dynamics Model (CDM) learns a prior CRF evolution pattern and guides motion diffusion sampling by aligning the generated motion's CRF with this learned prior.
On the FullBodyManipulation dataset, ContA-HOI yields more realistic and physically plausible HOIs, improving foot sliding and contact percentage over recent baselines.

Submission Number: 418