3DGH可分离生成头像与发型，支持自由组合编辑。
3DGH: 3D Head Generation with Composable Hair and Face
- 用模板化3D高斯点云分离建模发型与面部几何。
- 通过交叉注意力机制保持发型与脸型自然关联。
- 支持无条件生成和可编辑的3D发型合成，适合虚拟形象设计。
我们提出3DGH，一种无需条件输入的3D人头生成模型，可分别生成可组合的发型与面部。与以往将发型与面部耦合建模的方法不同，3DGH采用基于模板的3D高斯溅射表示，引入可变形发型几何以捕捉不同发型间的形变差异。基于此表示，设计了双生成器的3D GAN架构，并使用交叉注意力机制建模发型与面部之间的内在关联。模型在合成渲染图像上训练，通过精心设计的目标函数稳定训练并促进发型-面部解耦。大量实验验证了3DGH的设计合理性，定性与定量对比多个前沿3D GAN方法，证明其在无条件全头图像生成与可编辑3D发型编辑方面的有效性。
原文摘要 · Abstract (English)
We present 3DGH, an unconditional generative model for 3D human heads with composable hair and face components. Unlike previous work that entangles the modeling of hair and face, we propose to separate them using a novel data representation with template-based 3D Gaussian Splatting, in which deformable hair geometry is introduced to capture the geometric variations across different hairstyles. Based on this data representation, we design a 3D GAN-based architecture with dual generators and employ a cross-attention mechanism to model the inherent correlation between hair and face. The model is trained on synthetic renderings using carefully designed objectives to stabilize training and facilitate hair-face separation. We conduct extensive experiments to validate the design choice of 3DGH, and evaluate it both qualitatively and quantitatively by comparing with several state-of-the-art 3D GAN methods, demonstrating its effectiveness in unconditional full-head image synthesis and composable 3D hairstyle editing. More details will be available on our project page: https://c-he.github.io/projects/3dgh/.
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