We present a StyleGAN2-based deep learning approach for 3D shape generation,
called SDF-StyleGAN, with the aim of reducing visual and geometric
dissimilarity between generated shapes and a shape collection. We extend
StyleGAN2 to 3D generation and utilize the implicit signed distance function
(SDF) as the 3D shape representation, and introduce two novel global and local
shape discriminators that distinguish real and fake SDF values and gradients to
significantly improve shape geometry and visual quality. We further complement
the evaluation metrics of 3D generative models with the shading-image-based
Fr\'echet inception distance (FID) scores to better assess visual quality and
shape distribution of the generated shapes. Experiments on shape generation
demonstrate the superior performance of SDF-StyleGAN over the state-of-the-art.
We further demonstrate the efficacy of SDF-StyleGAN in various tasks based on
GAN inversion, including shape reconstruction, shape completion from partial
point clouds, single-view image-based shape generation, and shape style
editing. Extensive ablation studies justify the efficacy of our framework
design. Our code and trained models are available at
this https URL

介绍了一种基于 StyleGAN2 的深度学习方法来生成三维形状，称为 SDF-StyleGAN，使用隐式有符号距离函数（SDF）作为三维形状表示，并引入两种新的全局和局部形状鉴别器，在形状几何和视觉质量方面显著提高了性能。