Abstract
Recent advances in motion generation show remarkable progress. However, several limitations remain: (1) Existing pose-guided character motion transfer methods merely replicate motion without learning its style characteristics, resulting in inexpressive characters. (2) Motion style transfer methods rely heavily on motion capture data, which is difficult to obtain. (3) Generated motions sometimes violate physical laws. To address these challenges, this paper pioneers a new task: Video-to-Video Motion Personalization. We propose a novel framework, PersonaAnimator, which learns personalized motion patterns directly from unconstrained videos. This enables personalized motion transfer. To support this task, we introduce PersonaVid, the first video-based personalized motion dataset. It contains 20 motion content categories and 120 motion style categories. We further propose a Physics-aware Motion Style Regularization mechanism to enforce physical plausibility in the generated motions. Extensive experiments show that PersonaAnimator outperforms state-of-the-art motion transfer methods and sets a new benchmark for the Video-to-Video Motion Personalization task.
Abstract (translated)
最近的运动生成技术取得了显著进展，但仍然存在一些局限性：（1）现有的姿态引导的角色运动转移方法仅复制动作而不学习其风格特征，导致角色表现力不足。（2）运动风格转移方法严重依赖于难以获取的动作捕捉数据。（3）生成的运动有时会违反物理定律。为解决这些挑战，本文提出了一项新任务：视频到视频的个性化运动（Video-to-Video Motion Personalization）。我们提出了一个创新框架PersonaAnimator，该框架可以直接从无约束视频中学习个性化的运动模式，从而实现个性化的运动转移。为了支持这项任务，我们引入了PersonaVid，这是第一个基于视频的个性化动作数据集。它包含了20种不同的运动内容类别和120种不同的运动风格类别。此外，我们还提出了一种物理感知的动作风格正则化机制，以确保生成的动作符合物理可能性。大量的实验表明，PersonaAnimator在个性化的运动转移方法中表现优于现有的最新技术，并为视频到视频的个性化运动任务设立了新的基准。
URL
https://arxiv.org/abs/2508.19895
