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PAMotion: Physics-Aware Motion Generation for Full-Body Interaction with Multiple Objects
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-01 23:44
摘要
We present PAMotion, a physics-aware diffusion framework for generating realistic full-body human interactions with multiple objects.Existing diffusion-based methods that jointly synthesize human and object motions often struggle to capture the intricate physical interactions--especially those involving complex hand-object contacts. To address this issue, in this paper, we begin with our key observation: in everyday, slow-motion scenarios, object accelerations inherently reveal the underlying physical interactions.If an object's acceleration aligns with gravity, it is likely in free motion with no physical contact from human or other objects; otherwise, it must be in contact--directly or indirectly--with the human body. Building on this intuition, PAMotion jointly models full-body human motion, object motion, and their corresponding accelerations, enforcing physical plausibility through a physics-aware interaction loss.In this loss, we softly penalizes violations of consistency between object acceleration and human-object contact states. PAMotion follows a coarse-to-fine pipline: we first synthesize global torso and object translations, then conditionally refine hand motions and object rotations, achieving both high-level motion-text consistency and low-level physical fidelity. Experiments on two challenging datasets HIMO and ParaHome demonstrate that PAMotion achieves state-of-the-art performance in generating realistic, physically consistent full-body manipulation sequences involving multiple objects. Our code is released at https://github.com/liyuheng520/PAMotion.
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记录 #1 · 2026 · 最新观测版本
PAMotion: Physics-Aware Motion Generation for Full-Body Interaction with Multiple Objects
We present PAMotion, a physics-aware diffusion framework for generating realistic full-body human interactions with multiple objects.Existing diffusion-based methods that jointly synthesize human and object motions often struggle to capture the intricate physical interactions--especially those involving complex hand-object contacts. To address this issue, in this paper, we begin with our key observation: in everyday, slow-motion scenarios, object accelerations inherently reveal the underlying physical interactions.If an object's acceleration aligns with gravity, it is likely in free motion with no physical contact from human or other objects; otherwise, it must be in contact--directly or indirectly--with the human body. Building on this intuition, PAMotion jointly models full-body human motion, object motion, and their corresponding accelerations, enforcing physical plausibility through a physics-aware interaction loss.In this loss, we softly penalizes violations of consistency between object acceleration and human-object contact states. PAMotion follows a coarse-to-fine pipline: we first synthesize global torso and object translations, then conditionally refine hand motions and object rotations, achieving both high-level motion-text consistency and low-level physical fidelity. Experiments on two challenging datasets HIMO and ParaHome demonstrate that PAMotion achieves state-of-the-art performance in generating realistic, physically consistent full-body manipulation sequences involving multiple objects. Our code is released at https://github.com/liyuheng520/PAMotion.
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