CrossPortrait: Cross-Species Portrait Animation via Adversarial Motion Alignment

Cross-species portrait animation, which aims to animate a source portrait of species A using a driving video of species B, is particularly challenging due to substantial anatomical differences in facial structures across species. Previous works typically fine-tune human portrait animation models on animal video datasets, but often suffer from driving-species leakage. To address this, we propose a novel framework for expressive cross-species portrait animation, supporting diverse scenarios including human-to-animal, animal-to-human, and cross-species animal-to-animal (e.g., cat-to-dog) animation. Specifically, we decompose the portrait motion into rigid head poses and expression keypoints. To accommodate species-specific facial structures while facilitating cross-species expression alignment, we introduce species-routed expression encoders that incorporate species-specific LoRAs into a unified expression encoder. Furthermore, we introduce expression-level adversarial domain adaptation (ADA) to suppress driving-species-specific information in the expression representations, thereby mitigating species leakage and promoting a shared expression space across species. Extensive experiments demonstrate that our method enables expressive and identity-preserving portrait animation across diverse species combinations, while effectively reducing driving-species leakage.