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：运动重定向是角色动画中一个长期存在的问题，旨在将源角色的运动迁移并适配到目标角色上。典型应用是将现成运动序列通过迁移至新角色来生成动画。运动重定向也有望提升现有动画系统和运动数据库的互操作性——这些系统和数据库通常所采用的骨骼结构各不相同。此外，由于运动重定向的目标是抽象并迁移运动动力学，其有效解决方案可能与表达力强且功能强大的人体运动模型相吻合，这些模型更便于执行清理或编辑等操作。本文提出了一种新颖的人体运动抽象表示，该表示与骨骼拓扑和形态无关。基于Transformer架构，我们的模型能够编码和解码具有可变形态和拓扑的运动序列——从而拓展了重定向的应用范围——同时支持训练阶段未见的骨骼拓扑。具体而言，该模型采用自编码器结构，编码和解码过程分别以骨骼模板为条件，以提取并控制形态和拓扑信息。除了运动重定向，该模型还具有广泛的应用场景，因为其抽象表示为嵌入来自不同来源的运动数据提供了便利空间。它可能有助于多种数据驱动方法，使其能够结合稀缺的专项运动数据集（例如带有风格或接触标注的数据）与更大规模通用运动数据集，以提升性能和泛化能力。此外，我们证明该模型可用于超越重定向的其他应用，包括运动去噪和关节上采样。