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 this https URL to facilitate the
development and evaluation of this field.

通过将神经辐射场引入生成对抗网络的生成器中，采用模块化的生成过程并独立开发和替换每个模块的方法，对 3D-aware 图像合成的多种方法进行了研究和评估并发布了实现代码和模型，进而深化了我们对现有方法的理解和指出了进一步的研究方向。