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.

翻译：跨域人脸重定向需要对身份、表情和域特定风格属性进行解耦控制。现有方法通常在真实人脸数据上训练，存在以下局限：无法泛化至不同域、需要测试时优化，或依赖精心构建的多风格数据集进行微调以获得域不变的身份表示。本研究提出 \textit{StyleYourSmile}，一种新颖的单样本跨域人脸重定向方法，无需构建配对多风格数据。我们设计了一种高效的数据增强策略与双编码器框架，用于提取域不变的身份特征并捕获域特定的风格变化。利用这些解耦的控制信号，我们通过条件扩散模型实现跨域面部表情重定向。大量实验表明，\textit{StyleYourSmile} 在广泛的视觉域中实现了卓越的身份保持能力与重定向保真度。