StyleYourSmile: Cross-Domain Face Retargeting Without Paired Multi-Style Data

• URL: http://arxiv.org/abs/2512.01895v1
• Date: Mon, 01 Dec 2025 17:14:07 GMT
• Title: StyleYourSmile: Cross-Domain Face Retargeting Without Paired Multi-Style Data
• Authors: Avirup Dey, Vinay Namboodiri,
• Abstract summary: Cross-domain face requires disentangled control over identity, expressions, and domain-specific attributes.<n>textitStyleYourSmile achieves superior identity preservation and fidelity across a wide range of visual domains.
• Score: 0.22917707112773592
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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