Motion retargeting is the long-standing problem in character animation that 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 provide expressive and powerful human motion models in which operations such as cleaning or editing are easier. In this article, we present a novel neural network architecture for retargeting that extracts an 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 current 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长期存在的问题，旨在将源角色的运动转移并适配到另一目标角色。典型应用是将现成运动序列迁移至新角色以生成动画。运动重定向也有望增强现有动画系统与运动数据库的互操作性——因为它们所采用的骨架结构往往存在差异。此外，由于运动重定向的目标是抽象并传递运动动态特性，有效的解决方案可以提供表达力强且功能强大的人体运动模型，使得清洗或编辑等操作更加便捷。本文提出了一种新颖的运动重定向神经网络架构，能够提取与骨架拓扑和形态无关的人体运动抽象表示。基于变换器，我们的模型能够编码和解码具有可变形态与拓扑的运动序列——拓展了当前重定向的应用范围——同时支持训练阶段未见过的骨架拓扑结构。具体而言，模型采用自编码器结构，编码和解码过程分别以骨架模板为条件，以提取并控制形态与拓扑信息。除运动重定向外，该模型具有广泛应用潜力，因为其抽象表示可便利地嵌入不同来源的运动数据。这有望增强多种数据驱动方法，使其能够结合稀缺的专业运动数据集（如含风格或接触标注的数据）与更大的通用运动数据集，从而提升性能与泛化能力。此外，我们证明该模型在运动去噪和关节上采样等超越重定向的应用中同样有效。