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Found 5 papers, 3 papers with code
DiffusionAct: Controllable Diffusion Autoencoder for One-shot Face Reenactment
To this end, in this paper we present DiffusionAct, a novel method that leverages the photo-realistic image generation of diffusion models to perform neural face reenactment.
One-shot Neural Face Reenactment via Finding Directions in GAN's Latent Space
Moreover, we show that by embedding real images in the GAN latent space, our method can be successfully used for the reenactment of real-world faces.
HyperReenact: One-Shot Reenactment via Jointly Learning to Refine and Retarget Faces
In this paper, we present our method for neural face reenactment, called HyperReenact, that aims to generate realistic talking head images of a source identity, driven by a target facial pose.
StyleMask: Disentangling the Style Space of StyleGAN2 for Neural Face Reenactment
In this paper we address the problem of neural face reenactment, where, given a pair of a source and a target facial image, we need to transfer the target's pose (defined as the head pose and its facial expressions) to the source image, by preserving at the same time the source's identity characteristics (e. g., facial shape, hair style, etc), even in the challenging case where the source and the target faces belong to different identities.
Finding Directions in GAN's Latent Space for Neural Face Reenactment
Moreover, we show that by embedding real images in the GAN latent space, our method can be successfully used for the reenactment of real-world faces.
