从普通视频中学习个性化动作，让虚拟角色动起来更自然
PersonaAnimator: Personalized Motion Transfer from Unconstrained Videos
- 直接从无约束视频学习人物动作风格，无需专业采集数据
- 构建首个视频驱动的个性化动作数据集，含120种风格
- 引入物理合理性约束，生成动作更符合真实运动规律
近期动作生成技术取得显著进展，但仍存在三大瓶颈：(1) 现有基于姿态的动作迁移方法仅复制动作，未学习风格特征，导致角色表现力不足；(2) 动作风格迁移依赖难以获取的动作捕捉数据；(3) 生成动作有时违背物理规律。为此，本文提出新任务——视频到视频的动作个性化。我们提出PersonaAnimator框架，直接从无约束视频中学习个性化动作模式，实现动作风格迁移。为支持该任务，我们构建了首个视频驱动的个性化动作数据集PersonaVid，包含20种动作内容类别和120种动作风格类别。此外，提出物理感知的动作风格正则化机制，确保生成动作的物理合理性。大量实验表明，PersonaAnimator优于现有最先进方法，并在视频到视频动作个性化任务上建立新基准。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。