AEAvatar: Animatable and Editable Generative Gaussian Head Avatar

Abstract

The high photorealism and rendering efficiency of 3D Gaussian Splatting make it a promising approach for realistic 3D head avatar synthesis, leading to substantial progress in static avatar generation in recent works. However, current generation methods lack support for expression-driven animation, exhibit limited geometric and textural detail, and offer poor editability, which limits the practical applicability of this technology. Therefore, we propose AEAvatar, an Animatable and Editable Generative Gaussian Head Avatar framework for producing high-quality 3D avatars. AEAvatar employs a 2D GAN to generate per-pixel attribute maps, which are indexed via UV coordinates to produce 3D Gaussian attributes. The UV space is partitioned into static and dynamic regions, each used to index a separate set of Gaussians. Dynamic Gaussians are bound to the FLAME mesh to support facial deformation and animation, while static Gaussians represent rigid regions such as hair and shoulders. Thanks to its efficient feature mapping design, AEAvatar achieves high-quality animation at a resolution of \(512^{2}\) using only around 120K Gaussian primitives. This compact representation, coupled with an optimized caching mechanism, significantly boosts rendering efficiency, enabling real-time animation at up to 415 FPS. In addition, identity editing such as 3D face swapping can be easily performed by replacing either the dynamic or static set of Gaussians. These capabilities position AEAvatar as a practical and effective solution for synthesizing high-fidelity, real-time, and editable 3D head avatars. Project page: https://baoachun.github.io/AEAvatar/.