VRetouchEr: Learning Cross-frame Feature Interdependence with Imperfection Flow for Face Retouching in Videos

Face Video Retouching is a complex task that often requires labor-intensive manual editing. Conventional image retouching methods perform less satisfactorily in terms of generalization performance and stability when applied to videos without exploiting the correlation among frames. To address this issue, we propose a Video Retouching transformEr to remove facial imperfections in videos, which is referred to as VRetouchEr. Specifically, we estimate the apparent motion of imperfections between two consecutive frames, and the resulting displacement vectors are used to refine the imperfection map, which is synthesized from the current frame together with the corresponding encoder features. The flow-based imperfection refinement is critical for precise and stable retouching across frames. To leverage the temporal contextual information, we inject the refined imperfection map into each transformer block for multi-frame masked attention computation, such that we can capture the interdependence between the current frame and multiple reference frames. As a result, the imperfection regions can be replaced with normal skin with high fidelity, while at the same time keeping the other regions unchanged. Extensive experiments are performed to verify the superiority of VRetouchEr over state-of-the-art image retouching methods in terms of fidelity and stability.