Identity-Preserving Talking Head Cross-Identity Reenactment with Adaptive Structure Normalization

Talking Head reenactment aims to enable a face in a source image to animate motions in a driving frame. Existing warping-based methods generally utilize keypoints or landmarks as motion representations. However, the keypoints and landmarks inevitably contain conflicting facial structure that mislead cross-identity reenactment. In this paper, we propose a talking head generation method that aims to mitigate the structural differences for identity-preserving by predicting structure-adapted keypoints. The driving keypoints are adjusted by a proposed adaptive structure normalization module that aligns the statistics of the driving structural features with those of the source. Moreover, to provide paired samples for the cross-identity reenactment, we propose a well-designed cycle training pipeline by two steps, source to driving and reversed driving to another source from two videos. Extensive experiments demonstrate that our approach achieves an improvement of approximately 5% over state-of-the-art methods in identity preservation metric in cross-identity talking head reenactment.