PhysiGen: Integrating Collision-Aware Physical Constraints for High-Fidelity Human-Human Interaction Generation

arXiv cs.CV / 5/4/2026

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Key Points

  • Multi-person 3D human motion synthesis still struggles with realistic interactions, largely due to frequent body inter-penetration that reduces usability and visual realism.
  • The paper introduces PhysiGen, a general-purpose and computationally efficient optimization strategy that adds collision-aware physical constraints to human-human interaction generation.
  • PhysiGen reduces computation by approximating high-resolution body meshes with geometric primitives for faster inter-person collision detection.
  • It identifies collision regions to guide the optimization, and is designed to be plug-and-play with existing human interaction generation models.
  • Experiments across multiple datasets and models indicate PhysiGen substantially lowers interpenetration and improves visual coherence and physical plausibility over state-of-the-art methods.

Abstract

Despite substantial progress in text-driven 3D human motion synthesis, generating realistic multi-person interaction sequences remains challenging. Notably, body inter-penetration is a pervasive issue from both data acquisition to the generated results, which significantly undermines the realism and usability. Previous generative models either ignored this issue or introduced computationally expensive mesh-level loss functions to alleviate inter-body collisions. In this paper, we propose a general-purpose and computationally efficient optimization strategy named PhysiGen to explicitly integrate collision-aware physical constraints for human-human interaction generation. Specifically, we simplify the high-resolution human body mesh into geometric primitives to greatly reduce the cost of inter-person collision detection. Moreover, we identify the collision regions as the guidance of the optimization directions. PhysiGen is plug-and-play and can be readily integrated into existing human interaction generation models. Extensive cross-dataset and cross-model experiments show that our method can effectively reduce interpenetration and significantly improve visual coherence and physical plausibility compared to the state-of-the-art methods.

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