PAMotion：面向多物体全身交互的物理感知动作生成
PAMotion: Physics-Aware Motion Generation for Full-Body Interaction with Multiple Objects
把「物体加速度与接触状态的一致性」做成可微的物理感知损失，并采用由粗到细的分阶段条件生成（先躯干与物体平移、再手部与物体旋转），这套思路可直接迁移到任何需要物理合理性的扩散式人体/物体联合运动生成任务。
原文依据 we jointly model full-body human motion, object motion, and their corresponding accelerations, enforcing physical plausibility through a physics-aware interaction loss
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论我们提出 PAMotion，一个物理感知的扩散框架，用于生成真实的多物体全身人体交互。现有联合合成人体与物体运动的扩散方法往往难以刻画复杂的物理交互，尤其是涉及复杂手-物接触的情形。为解决该问题，本文从我们的关键观察出发：在日常的慢速运动场景中，物体加速度本身就揭示了潜在的物理交互。若物体加速度与重力方向一致，则该物体很可能处于自由运动状态，未受到人体或其他物体的物理接触；否则，它必然与人体存在直接或间接的接触。基于这一直觉，PAMotion 联合建模全身人体运动、物体运动及其对应的加速度，并通过物理感知交互损失来施加物理合理性约束。在该损失中，我们对物体加速度与人物接触状态之间一致性被违反的情况进行软惩罚。PAMotion 采用由粗到细的流程：我们首先合成全局躯干与物体平移，然后以此为条件细化手部运动与物体旋转，从而同时实现高层面的运动-文本一致性与低层面的物理保真度。在 HIMO 与 ParaHome 两个具有挑战性的数据集上的实验表明，PAMotion 在生成涉及多物体的真实且物理一致的全身操作序列方面取得了最优性能。我们的代码已发布于 https://github.com/liyuheng520/PAMotion。
英文原文摘要 1593 字符
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.