A good motion retargeting cannot be reached without reasonable consideration of source-target differences on both the skeleton and shape geometry levels. In this work, we propose a novel Residual RETargeting network (R2ET) structure, which relies on two neural modification modules, to adjust the source motions to fit the target skeletons and shapes progressively. In particular, a skeleton-aware module is introduced to preserve the source motion semantics. A shape-aware module is designed to perceive the geometries of target characters to reduce interpenetration and contact-missing. Driven by our explored distance-based losses that explicitly model the motion semantics and geometry, these two modules can learn residual motion modifications on the source motion to generate plausible retargeted motion in a single inference without post-processing. To balance these two modifications, we further present a balancing gate to conduct linear interpolation between them. Extensive experiments on the public dataset Mixamo demonstrate that our R2ET achieves the state-of-the-art performance, and provides a good balance between the preservation of motion semantics as well as the attenuation of interpenetration and contact-missing. Code is available at https://github.com/Kebii/R2ET.

翻译：良好的运动重定向离不开对源-目标在骨骼与形状几何层面差异的合理考量。本研究提出一种新颖的残差重定向网络（R2ET）结构，该结构依托两个神经修正模块，逐步调整源运动以适配目标骨骼与形状。具体而言，引入骨骼感知模块以保留源运动语义，设计形状感知模块以感知目标角色的几何特性，从而减少穿插与接触缺失现象。在显式建模运动语义与几何的基于距离的损失函数驱动下，这两个模块可学习源运动的残差修正，在无需后处理的情况下通过单次推理生成合理的重定向运动。为平衡两种修正，我们进一步提出平衡门控机制，在其间进行线性插值。在公开数据集Mixamo上的大量实验表明，R2ET达到了最先进性能，并在运动语义保留、穿插与接触缺失衰减之间实现了良好平衡。代码见https://github.com/Kebii/R2ET。