  Face de-identification in videos is a challenging task in the domain of
computer vision, primarily used in privacy-preserving applications. Despite the
considerable progress achieved through generative vision models, there remain
multiple challenges in the latest approaches. They lack a comprehensive
discussion and evaluation of aspects such as realism, temporal coherence, and
preservation of non-identifiable features. In our work, we propose RID-Twin: a
novel pipeline that leverages the state-of-the-art generative models, and
decouples identity from motion to perform automatic face de-identification in
videos. We investigate the task from a holistic point of view and discuss how
our approach addresses the pertinent existing challenges in this domain. We
evaluate the performance of our methodology on the widely employed VoxCeleb2
dataset, and also a custom dataset designed to accommodate the limitations of
certain behavioral variations absent in the VoxCeleb2 dataset. We discuss the
implications and advantages of our work and suggest directions for future
research.
