Retargeting motion across characters with varying body shapes while preserving interaction semantics, such as self-contact and near-body proximity, remains a challenging problem. While recent geometry-aware approaches address this by maintaining spatial relationships between predefined corresponding regions, their reliance on static correspondences often struggles when the target character exhibits exaggerated body proportions. In this paper, we present a geometry-aware motion retargeting framework that preserves interaction semantics by performing proximity matching over spatially adaptive anchors. Unlike prior methods with static anchor definitions, the proposed method dynamically repositions anchors to reachable regions on the target character. This is achieved via a Transformer-based anchor refinement strategy that predicts anchor displacements and constrains the translated anchors to remain on the target character geometry through differentiable soft projection. By incorporating pose-dependent spatial structures from the source character, the adapted anchors provide structurally coherent guidance for interaction-aware retargeting. Conditioned on these anchors, a graph-based autoencoder predicts target skeletal motion that preserves the spatial configuration of the source. To encourage task-aligned optimization between anchor adaptation and motion retargeting, we adopt an alternating training scheme in which each module is optimized in turn. Through extensive evaluations, we demonstrate that our method outperforms state-of-the-art approaches in preserving interaction fidelity across diverse character geometries.

翻译：跨具有不同身体形状的角色进行运动重定向，同时保留交互语义（如自身接触和近身接近），仍然是一个具有挑战性的问题。尽管最近基于几何感知的方法通过维护预定义对应区域之间的空间关系来解决这一问题，但当目标角色表现出夸张的身体比例时，这些方法对静态对应关系的依赖常常失效。本文提出了一种基于几何感知的运动重定向框架，通过在空间自适应锚点上进行邻近匹配来保留交互语义。与采用静态锚点定义的先前方法不同，所提出的方法动态地将锚点重新定位到目标角色上的可达区域。这是通过一种基于Transformer的锚点细化策略实现的，该策略预测锚点位移，并通过可微软投影将平移后的锚点约束在目标角色几何体上。通过结合源角色的姿态相关空间结构，自适应锚点为交互感知重定向提供了结构连贯的引导。在这些锚点的约束下，一个基于图的自动编码器预测保留源空间配置的目标骨骼运动。为了促进锚点适配与运动重定向之间的任务对齐优化，我们采用了一个交替训练方案，其中每个模块轮流优化。通过广泛评估，我们证明我们的方法在跨不同角色几何体保留交互保真度方面优于最先进的方法。