DiffusionAct: Controllable Diffusion Autoencoder for One-shot Face Reenactment

• URL: http://arxiv.org/abs/2403.17217v1
• Date: Mon, 25 Mar 2024 21:46:53 GMT
• Title: DiffusionAct: Controllable Diffusion Autoencoder for One-shot Face Reenactment
• Authors: Stella Bounareli, Christos Tzelepis, Vasileios Argyriou, Ioannis Patras, Georgios Tzimiropoulos,
• Abstract summary: Video-driven neural face reenactment aims to synthesize realistic facial images that successfully preserve the identity and appearance of a source face. Recent advances in Diffusion Probabilistic Models (DPMs) enable the generation of high-quality realistic images. We present Diffusion, a novel method that leverages the photo-realistic image generation of diffusion models to perform neural face reenactment.
• Score: 34.821255203019554
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Video-driven neural face reenactment aims to synthesize realistic facial images that successfully preserve the identity and appearance of a source face, while transferring the target head pose and facial expressions. Existing GAN-based methods suffer from either distortions and visual artifacts or poor reconstruction quality, i.e., the background and several important appearance details, such as hair style/color, glasses and accessories, are not faithfully reconstructed. Recent advances in Diffusion Probabilistic Models (DPMs) enable the generation of high-quality realistic images. 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. Specifically, we propose to control the semantic space of a Diffusion Autoencoder (DiffAE), in order to edit the facial pose of the input images, defined as the head pose orientation and the facial expressions. Our method allows one-shot, self, and cross-subject reenactment, without requiring subject-specific fine-tuning. We compare against state-of-the-art GAN-, StyleGAN2-, and diffusion-based methods, showing better or on-par reenactment performance.

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