PSAIP: Prior Structure-Assisted Identity-Preserving Network for Face Animation

Face animation aims to render the source image according to the motion of the driving images while preserving the identity information of the source image. Despite significant advancements with the introduction of additional information, the literature lacks a thorough exploration of identity preservation. This paper proposes a Prior Structure-Assisted Identity-Preserving (PSAIP) network for face animation. Specifically, we introduce the additional priors of portrait segmentation images and face landmarks, which provide rich face geometric information while masking background interference to generate more realistic face images. Furthermore, we design an identity-enhancing generator network to adaptively fuses the identity feature information extracted from the source portrait and motion representations. Also, we utilize an identity-aware feature loss based on segmentation to avoid background distractions and monitor the identity preservation ability of the model. Extensive experiments on the VoxCeleb dataset and HDTF dataset show that our method is superior in identity preservation and realistic visual effects.