Motion retargeting is a promising approach for generating natural and compelling animations for nonhuman characters. However, it is challenging to translate human movements into semantically equivalent motions for target characters with different morphologies due to the ambiguous nature of the problem. This work presents a novel learning-based motion retargeting framework, Adversarial Correspondence Embedding (ACE), to retarget human motions onto target characters with different body dimensions and structures. Our framework is designed to produce natural and feasible robot motions by leveraging generative-adversarial networks (GANs) while preserving high-level motion semantics by introducing an additional feature loss. In addition, we pretrain a robot motion prior that can be controlled in a latent embedding space and seek to establish a compact correspondence. We demonstrate that the proposed framework can produce retargeted motions for three different characters -- a quadrupedal robot with a manipulator, a crab character, and a wheeled manipulator. We further validate the design choices of our framework by conducting baseline comparisons and a user study. We also showcase sim-to-real transfer of the retargeted motions by transferring them to a real Spot robot.

翻译：摘要：运动重定向是一种为生成非人类角色自然且富有表现力动画的有效方法。然而，由于问题的模糊性，将人类运动转化为具有不同形态的目标角色语义等效运动极具挑战性。本研究提出一种新颖的基于学习的运动重定向框架——对抗性对应嵌入（ACE），旨在将人类运动重定向到具有不同身体尺寸和结构的目标角色上。该框架通过利用生成对抗网络（GANs）产生自然且可行的机器人运动，同时引入额外特征损失以保留高层运动语义。此外，我们预训练了一个可在潜在嵌入空间中控制的机器人运动先验，并致力于建立紧凑的对应关系。实验表明，所提框架可为三种不同角色（带机械臂的四足机器人、螃蟹角色和轮式机械臂）生成重定向运动。通过基线对比实验和用户研究，我们进一步验证了框架设计选择的合理性。最后，我们展示了将重定向运动迁移至真实Spot机器人的仿真到现实迁移结果。