无需配对数据，一键实现跨风格人脸表情迁移。
StyleYourSmile: Cross-Domain Face Retargeting Without Paired Multi-Style Data
- 用双编码器分离身份与风格，提取域不变特征。
- 在多个视觉领域上保持高身份保真度和表情迁移精度。
- 适合需要快速跨风格生成的数字人、影视特效应用。
跨域人脸重定向需解耦控制身份、表情与域特定风格属性。现有方法通常在真实人脸数据上训练，或泛化能力差，或需测试时优化，或依赖精心构建的多风格配对数据集才能获得域不变的身份表示。本文提出新方法 StyleYourSmile，一种单次输入的跨域人脸重定向技术，无需依赖人工标注的多风格配对数据。通过高效数据增强策略与双编码器框架，提取域不变身份特征并捕捉域特定风格变化。利用这些解耦控制信号，条件化扩散模型实现跨域表情迁移。大量实验表明，StyleYourSmile 在多种视觉域下均实现了优异的身份保留与重定向保真度。
原文摘要 · Abstract (English)
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.
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