UniFaceGAN: High-Quality 3D Face Editing With a Unified Latent Space

Abstract

Recent advancements in 3D face generation have explored various representation and generative models. However, these methods often offer limited 3D face editing capabilities. In this paper, we introduce UniFaceGAN, a novel framework for 3D facial editing, leveraging a unified latent space to facilitate diverse and user-friendly 3D facial manipulation. The key to efficient 3D facial editing lies in establishing a representation space that offers essential facial priors. To achieve this, we propose encoding high-dimensional 3D faces into a compact, disentangled latent space which is learned through conditional 3D GANs guided by text descriptions. With the help of the GAN inversion techniques, UniFaceGAN allows us to edit existing 3D faces, accompanied by a residual editing strategy to mitigate inversion errors efficiently. We demonstrate UniFaceGAN can generate high-quality 3D faces and supports various 3D face editing applications, including CLIP-based stylizations, multiple-point-based drag manipulation, and local blending among multiple faces.