ViCoFace: Learning Disentangled Latent Motion Representations for Visual-Consistent Face Reenactment

Unsupervised face reenactment aims to animate a source image to imitate the motions of a target image while retaining the source portrait’s attributes like facial geometry, identity, hair texture, and background. While prior methods can extract the motion from the target image via compact representations (e.g., key-points or latent motion bases 50), they are not robust in predicting motions that are disentangled with portrait attributes, thus failing to preserve portrait attributes in the cross-subject reenactment. In this work, we propose an effective and cost-efficient face reenactment approach to address this issue. Our approach is highlighted by two major strengths. First, based on the theory of latent-motion bases, we disentangle the full-head motion into two parts: the transferable motion and preservable motion and then compose the full motion representation using latent motions from the source image and the target image. Second, to optimize and learn disentangled motions, we introduce an efficient training framework, which features two training strategies 1) a mixture training strategy that encompasses self-reenactment training and cross-subject training for better motion disentanglement; and 2) a multi-path training strategy that improves the visual consistency of portrait attributes. Extensive experiments on widely used benchmarks demonstrate that our method exhibits a remarkable generalization ability compared to state-of-the-art baselines. Project and demos are available at https://junleen.github.io/projects/vicoface .