Motion retargeting holds a premise of offering a larger set of motion data for characters and robots with different morphologies. Many prior works have approached this problem via either handcrafted constraints or paired motion datasets, limiting their applicability to humanoid characters or narrow behaviors such as locomotion. Moreover, they often assume a fixed notion of retargeting, overlooking domain-specific objectives like style preservation in animation or task-space alignment in robotics. In this work, we propose MoReFlow, Motion Retargeting via Flow Matching, an unsupervised framework that learns correspondences between characters' motion embedding spaces. Our method consists of two stages. First, we train tokenized motion embeddings for each character using a VQ-VAE, yielding compact latent representations. Then, we employ flow matching with conditional coupling to align the latent spaces across characters, which simultaneously learns conditioned and unconditioned matching to achieve robust but flexible retargeting. Once trained, MoReFlow enables flexible and reversible retargeting without requiring paired data. Experiments demonstrate that MoReFlow produces high-quality motions across diverse characters and tasks, offering improved controllability, generalization, and motion realism compared to the baselines.

翻译：运动重定向旨在为不同形态的角色与机器人提供更丰富的运动数据。许多先前工作通过手工约束或配对运动数据集处理此问题，这限制了其仅适用于人形角色或如步态等狭窄行为范围。此外，这些方法通常假设固定的重定向概念，忽视了领域特定目标，例如动画中的风格保持或机器人学中的任务空间对齐。本文提出MoReFlow（基于流匹配的运动重定向），一种无监督框架，通过学习角色运动嵌入空间之间的对应关系实现重定向。我们的方法包含两个阶段：首先，利用VQ-VAE为每个角色训练令牌化运动嵌入，得到紧凑的潜在表示；随后，采用条件耦合的流匹配技术对不同角色的潜在空间进行对齐，该过程同时学习条件与非条件匹配，以实现鲁棒而灵活的重定向。训练完成后，MoReFlow无需配对数据即可实现灵活可逆的运动重定向。实验表明，相较于基线方法，MoReFlow能在多样化的角色与任务中生成高质量运动，并在可控性、泛化能力与运动真实感方面均有提升。