Recently 3D-aware GAN methods with neural radiance field have developed
rapidly. However, current methods model the whole image as an overall neural
radiance field, which limits the partial semantic editability of synthetic
results. Since NeRF renders an image pixel by pixel, it is possible to split
NeRF in the spatial dimension. We propose a Compositional Neural Radiance Field
(CNeRF) for semantic 3D-aware portrait synthesis and manipulation. CNeRF
divides the image by semantic regions and learns an independent neural radiance
field for each region, and finally fuses them and renders the complete image.
Thus we can manipulate the synthesized semantic regions independently, while
fixing the other parts unchanged. Furthermore, CNeRF is also designed to
decouple shape and texture within each semantic region. Compared to
state-of-the-art 3D-aware GAN methods, our approach enables fine-grained
semantic region manipulation, while maintaining high-quality 3D-consistent
synthesis. The ablation studies show the effectiveness of the structure and
loss function used by our method. In addition real image inversion and cartoon
portrait 3D editing experiments demonstrate the application potential of our
method.

通过分割语义区域并利用神经辐射场学习各自的辐射场，最后合并成完整图像，CNeRF 方法可以实现细粒度的语义区域操作，同时维持高质量的 3D 一致合成。