用部件注意力统一不同骨骼的动捕重定向，保持动作语义和真实感。
PALUM: Part-based Attention Learning for Unified Motion Retargeting
- 将关节划分为身体部件，用注意力捕捉时空关系。
- 在未见过的骨骼-动作组合上仍保持高保真度与语义一致性。
- 适合动画师快速迁移动作到不同角色，尤其跨骨骼结构场景。
在不同骨架结构的角色间重定向动作是计算机动画中的基础挑战。当源角色与目标角色骨骼布局差异较大时，保持原始动作的语义与质量变得愈发困难。本文提出PALUM，一种新方法：通过将关节划分为语义身体部件，并应用注意力机制捕捉时空关系，学习跨多样骨架拓扑的通用运动表示。该方法借助与骨架无关的表示及目标特定结构信息，实现动作向目标骨架的转移。为确保学习鲁棒性并保持动作保真度，引入循环一致性机制以维持整个重定向过程中的语义连贯性。大量实验表明，其在处理多样化骨骼结构时表现优异，即使在泛化至此前未见的骨架-动作组合时，仍能保持动作的真实感与语义保真度。我们将公开实现代码以支持后续研究。
原文摘要 · Abstract (English)
Retargeting motion between characters with different skeleton structures is a fundamental challenge in computer animation. When source and target characters have vastly different bone arrangements, maintaining the original motion's semantics and quality becomes increasingly difficult. We present PALUM, a novel approach that learns common motion representations across diverse skeleton topologies by partitioning joints into semantic body parts and applying attention mechanisms to capture spatio-temporal relationships. Our method transfers motion to target skeletons by leveraging these skeleton-agnostic representations alongside target-specific structural information. To ensure robust learning and preserve motion fidelity, we introduce a cycle consistency mechanism that maintains semantic coherence throughout the retargeting process. Extensive experiments demonstrate superior performance in handling diverse skeletal structures while maintaining motion realism and semantic fidelity, even when generalizing to previously unseen skeleton-motion combinations. We will make our implementation publicly available to support future research.
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