Generative Neural Radiance Field (GNeRF) models, which extract implicit 3D
representations from 2D images, have recently been shown to produce realistic
images representing rigid/semi-rigid objects, such as human faces or cars.
However, they usually struggle to generate high-quality images representing
non-rigid objects, such as the human body, which is of a great interest for
many computer graphics applications. This paper proposes a 3D-aware
Semantic-Guided Generative Model (3D-SGAN) for human image synthesis, which
combines a GNeRF with a texture generator. The former learns an implicit 3D
representation of the human body and outputs a set of 2D semantic segmentation
masks. The latter transforms these semantic masks into a real image, adding a
realistic texture to the human appearance. Without requiring additional 3D
information, our model can learn 3D human representations with a
photo-realistic, controllable generation. Our experiments on the DeepFashion
dataset show that 3D-SGAN significantly outperforms the most recent baselines.
The code is available at this https URL

提出了一种基于 3D-SGAN 的语义指导生成模型，利用生成神经辐射场（GNeRF）提取人体的隐式 3D 表示，并用纹理生成器将语义掩码转换为真实图像，为计算机图形学应用中的非刚性物体生成具有照片级真实感的图像。在 DeepFashion 数据集上，比最新的基准表现显著优秀。