Physically Plausible Human-Object Interaction Generation via Attribute Classifier Guidance

Kengo Ikeuchi, Takehiko Ohkawa, Risa Shinoda, Yoichi Sato

Keywords: Human-Object Interaction, Classifier Guidance

Abstract:

Human motion during daily interactions is inherently shaped by physical attributes such as mass, friction, and fragility.
While the field of human motion modeling has advanced with recent motion diffusion models, particularly in human-object interactions, these models rely solely on surface-level geometry and often fail to account for underlying physical responses.
Critically, variations in mass lead to a wide range of dynamic behaviors, a factor that remains underexplored in existing studies.
To address this, we propose Attribute Classifier Guidance, a plug-and-play framework that adapts large pre-trained motion diffusion models for physical-attribute-aware synthesis.
Specifically, our approach steers the diffusion sampling process using the gradients of a lightweight, attribute-specific classifier.
We validate our framework on object mass by analyzing mass-sensitive kinematics on existing datasets, using pelvis height to reflect center-of-mass shifts and spine lean angle to measure postural counterbalancing.
Our experiments demonstrate that our approach improves physical realism, such as promoting more upright postures for lighter objects, while maintaining competitive overall generation quality.

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Submission Number: 8