StyleYourSmile: Cross-Domain Face Retargeting Without Paired Multi-Style Data
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914176263258112 |
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| author | Dey, Avirup Namboodiri, Vinay |
| author_facet | Dey, Avirup Namboodiri, Vinay |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_01895 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | StyleYourSmile: Cross-Domain Face Retargeting Without Paired Multi-Style Data Dey, Avirup Namboodiri, Vinay Computer Vision and Pattern Recognition 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. |
| title | StyleYourSmile: Cross-Domain Face Retargeting Without Paired Multi-Style Data |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.01895 |