Benchmarking and Analyzing 3D-aware Image Synthesis with a Modularized
  Codebase
Qiuyu Wang Zifan Shi Kecheng Zheng Yinghao Xu Sida Peng Yujun Shen
Despite the rapid advance of 3D-aware image synthesis, existing studies
usually adopt a mixture of techniques and tricks, leaving it unclear how each
part contributes to the final performance in terms of generality. Following the
most popular and effective paradigm in this field, which incorporates a neural
radiance field (NeRF) into the generator of a generative adversarial network
(GAN), we build a well-structured codebase, dubbed Carver, through modularizing
the generation process. Such a design allows researchers to develop and replace
each module independently, and hence offers an opportunity to fairly compare
various approaches and recognize their contributions from the module
perspective. The reproduction of a range of cutting-edge algorithms
demonstrates the availability of our modularized codebase. We also perform a
variety of in-depth analyses, such as the comparison across different types of
point feature, the necessity of the tailing upsampler in the generator, the
reliance on the camera pose prior, etc., which deepen our understanding of
existing methods and point out some further directions of the research work. We
release code and models at https://github.com/qiuyu96/Carver to facilitate the
development and evaluation of this field.