StyleYourSmile: Diffusion-Driven One Shot Cross-Domain Retargeting for Portraits

Cross-domain portrait retargeting requires disentangled control over identity, expressions, and domain-specific stylistic attributes. Existing methods, typically trained on subjects in a single domain, either fail to generalize across image styles, need test-time optimizations, or require fine-tuning with curated multi-style data to achieve domain-invariant identity representations. In this work, we introduce StyleYourSmile, a novel one-shot cross-domain face retargeting method that eliminates these bottlenecks. We propose a dual-encoder architecture alongside an efficient data augmentation strategy for representing 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 StyleYourSmile achieves superior identity preservation and retargeting fidelity across a wide range of visual styles.