StyleYourSmile: 跨域人脸重定向无需配对多风格数据
StyleYourSmile: Cross-Domain Face Retargeting Without Paired Multi-Style Data
AI总结：
StyleYourSmile通过双编码器框架和高效数据增强策略，实现无需多风格配对数据的跨域人脸重定向，提升身份保持与重定向保真度。
摘要：
跨域人脸重定向需要对身份、表情和领域特定风格属性进行解耦控制。现有方法通常在现实人脸上训练，要么无法跨领域泛化，要么需要测试时优化，或者需要微调精心编纂的多风格数据集才能获得领域不变的身份表示。在本工作中，我们引入了StyleYourSmile，一种新颖的一次性跨域人脸重定向方法，消除了对精心编纂的多风格配对数据的需求。我们提出了一种高效的数据显示策略以及双编码器框架，用于提取领域不变的身份线索并捕捉领域特定的风格变化。利用这些解耦的控制信号，我们对扩散模型进行条件化以在不同领域之间重定向面部表情。广泛的实验表明，StyleYourSmile在广泛的视觉领域中实现了优越的身份保持和重定向保真度。
英文摘要：
Cross-domain face retargeting requires disentangled control over identity, expressions, and domain-specific stylistic attributes. Existing methods, typically trained on real-world faces, either fail to generalize across domains, need test-time optimizations, or require fine-tuning with carefully curated multi-style datasets to achieve domain-invariant identity representations. In this work, we introduce \textit{StyleYourSmile}, a novel one-shot cross-domain face retargeting method that eliminates the need for curated multi-style paired data. We propose an efficient data augmentation strategy alongside a dual-encoder framework, for extracting domain-invariant identity cues and capturing domain-specific stylistic variations. Leveraging these disentangled control signals, we condition a diffusion model to retarget facial expressions across domains. Extensive experiments demonstrate that \textit{StyleYourSmile} achieves superior identity preservation and retargeting fidelity across a wide range of visual domains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
