Motion retargeting is a long-standing problem in character animation, which consists in transferring and adapting the motion of a source character to another target character. A typical application is the creation of motion sequences from off-the-shelf motions by transferring them onto new characters. Motion retargeting is also promising to increase interoperability of existing animation systems and motion databases, as they often differ in the structure of the skeleton(s) considered. Moreover, since the goal of motion retargeting is to abstract and transfer motion dynamics, effective solutions might coincide with expressive and powerful human motion models in which operations such as cleaning or editing are easier. In this article, we present a novel abstract representation of human motion agnostic to skeleton topology and morphology. Based on transformers, our model is able to encode and decode motion sequences with variable morphology and topology -- extending the scope of retargeting -- while supporting skeleton topologies not seen during the training phase. More specifically, our model is structured as an autoencoder and encoding and decoding are separately conditioned on skeleton templates to extract and control morphology and topology. Beyond motion retargeting, our model has many applications since our abstract representation is a convenient space to embed motion data from different sources. It may potentially be benefical to a number of data-driven methods, allowing them to combine scarce specialised motion datasets (e.g. with style or contact annotations) and larger general motion datasets for improved performance and generalisation ability. Moreover, we show that our model can be useful for other applications beyond retargeting, including motion denoising and joint upsampling.

翻译：摘要：运动重定向是角色动画中的一个长期难题，其核心在于将源角色的运动迁移并适配至目标角色。典型应用包括将现成动作序列通过迁移至新角色来创建动画片段。由于现有动画系统及运动数据库所采用的骨骼结构存在差异，运动重定向技术有望提升系统间的互操作性。此外，由于运动重定向旨在抽象并传递运动动力学特性，有效的解决方案可能同时构成更强大的人体运动表达模型，从而简化清洗、编辑等操作。本文提出了一种与骨骼拓扑结构和形态无关的人体运动抽象表征新方法。该模型基于Transformer架构，可编码和解码具有可变形态与拓扑结构的运动序列——拓展了重定向的应用范围——同时支持训练阶段未见过的骨骼拓扑。具体而言，模型采用自编码器结构，编码与解码过程分别以骨骼模板为条件，从而提取并控制形态与拓扑信息。除运动重定向外，该模型具有广泛的应用前景：抽象表征空间为嵌入不同来源的运动数据提供了便捷载体，有望惠及多种数据驱动方法，使其能够融合稀缺专用数据集（如带有风格或接触标注）与大规模通用运动数据集，从而提升性能与泛化能力。此外，我们证明了该模型在运动去噪及关节上采样等重定向之外的任务中同样具有实用价值。