Capturing and maintaining geometric interactions among different body parts is crucial for successful motion retargeting in skinned characters. Existing approaches often overlook body geometries or add a geometry correction stage after skeletal motion retargeting. This results in conflicts between skeleton interaction and geometry correction, leading to issues such as jittery, interpenetration, and contact mismatches. To address these challenges, we introduce a new retargeting framework, MeshRet, which directly models the dense geometric interactions in motion retargeting. Initially, we establish dense mesh correspondences between characters using semantically consistent sensors (SCS), effective across diverse mesh topologies. Subsequently, we develop a novel spatio-temporal representation called the dense mesh interaction (DMI) field. This field, a collection of interacting SCS feature vectors, skillfully captures both contact and non-contact interactions between body geometries. By aligning the DMI field during retargeting, MeshRet not only preserves motion semantics but also prevents self-interpenetration and ensures contact preservation. Extensive experiments on the public Mixamo dataset and our newly-collected ScanRet dataset demonstrate that MeshRet achieves state-of-the-art performance. Code available at https://github.com/abcyzj/MeshRet.

翻译：在蒙皮角色中捕捉并保持不同身体部位间的几何交互对于成功的运动重定向至关重要。现有方法往往忽视身体几何结构，或在骨骼运动重定向后添加几何校正阶段。这导致骨骼交互与几何校正之间的冲突，从而产生抖动、相互穿透和接触失配等问题。为解决这些挑战，我们提出了一个新的重定向框架MeshRet，该框架直接在运动重定向中对密集几何交互进行建模。首先，我们使用语义一致传感器（SCS）在不同角色间建立密集网格对应关系，该方法能有效适应多样的网格拓扑结构。随后，我们开发了一种新颖的时空表示，称为密集网格交互（DMI）场。该场作为交互SCS特征向量的集合，巧妙地捕捉了身体几何间的接触与非接触交互。通过在重定向过程中对齐DMI场，MeshRet不仅能保持运动语义，还能防止自穿透并确保接触保持。在公开的Mixamo数据集及我们新收集的ScanRet数据集上进行的大量实验表明，MeshRet实现了最先进的性能。代码发布于 https://github.com/abcyzj/MeshRet。