Identity-Aware and Shape-Aware Propagation of Face Editing in Videos.

The development of deep generative models has inspired various facial image editing methods, but many of them are difficult to be directly applied to video editing due to various challenges ranging from imposing 3D constraints, preserving identity consistency, ensuring temporal coherence, etc. To address these challenges, we propose a new framework operating on the StyleGAN2 latent space for identity-aware and shape-aware edit propagation on face videos. In order to reduce the difficulties of maintaining the identity, keeping the original 3D motion, and avoiding shape distortions, we disentangle the StyleGAN2 latent vectors of human face video frames to decouple the appearance, shape, expression, and motion from identity. An edit encoding module is used to map a sequence of image frames to continuous latent codes with 3D parametric control and is trained in a self-supervised manner with identity loss and triple shape losses. Our model supports propagation of edits in various forms: I. direct appearance editing on a specific keyframe, II. implicit editing of face shape via a given reference image, and III. existing latent-based semantic edits. Experiments show that our method works well for various forms of videos in the wild and outperforms an animation-based approach and the recent deep generative techniques.

Human Motion Transfer With 3D Constraints and Detail Enhancement.

We propose a new method for realistic human motion transfer using a generative adversarial network (GAN), which generates a motion video of a target character imitating actions of a source character, while maintaining high authenticity of the generated results. We tackle the problem by decoupling and recombining the posture information and appearance information of both the source and target characters. The innovation of our approach lies in the use of the projection of a reconstructed 3D human model as the condition of GAN to better maintain the structural integrity of transfer results in different poses. We further introduce a detail enhancement net to enhance the details of transfer results by exploiting the details in real source frames. Extensive experiments show that our approach yields better results both qualitatively and quantitatively than the state-of-the-art methods.