用3D高斯点云实现可编辑的逼真头像生成
Generating Editable Head Avatars with 3D Gaussian GANs
- 用3D高斯点云替代隐式场，提升头像变形与编辑灵活性
- 结合3DMM与纹理图，实现表情精准控制和身份保持
- 支持头发等复杂结构建模，适合影视动画与虚拟人应用
生成可驱动且可编辑的3D头像在计算机视觉与图形学中有广泛应用。传统3D感知生成对抗网络（GAN）常采用隐式场如神经辐射场（NeRF），虽能生成逼真、视角一致的3D头像，但存在变形灵活性差、编辑困难的问题。本文提出一种新方法，将显式3D表示3D高斯点云（3DGS）引入生成框架，显著提升光照控制能力与编辑自由度。核心为可编辑高斯头像（EG-Head）模型，融合3D形态模型（3DMM）与纹理图，实现精确的表情控制与灵活的纹理编辑，同时保留身份特征。为捕捉头发等非面部复杂几何结构，引入辅助3DGS与三平面特征。大量实验表明，本方法在3D感知合成质量与可控性方面达到当前最优水平。代码与模型已开源：https://github.com/liguohao96/EGG3D。
原文摘要 · Abstract (English)
Generating animatable and editable 3D head avatars is essential for various applications in computer vision and graphics. Traditional 3D-aware generative adversarial networks (GANs), often using implicit fields like Neural Radiance Fields (NeRF), achieve photorealistic and view-consistent 3D head synthesis. However, these methods face limitations in deformation flexibility and editability, hindering the creation of lifelike and easily modifiable 3D heads. We propose a novel approach that enhances the editability and animation control of 3D head avatars by incorporating 3D Gaussian Splatting (3DGS) as an explicit 3D representation. This method enables easier illumination control and improved editability. Central to our approach is the Editable Gaussian Head (EG-Head) model, which combines a 3D Morphable Model (3DMM) with texture maps, allowing precise expression control and flexible texture editing for accurate animation while preserving identity. To capture complex non-facial geometries like hair, we use an auxiliary set of 3DGS and tri-plane features. Extensive experiments demonstrate that our approach delivers high-quality 3D-aware synthesis with state-of-the-art controllability. Our code and models are available at https://github.com/liguohao96/EGG3D.
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