Unsupervised learning of 3D-aware generative adversarial networks has lately
made much progress. Some recent work demonstrates promising results of learning
human generative models using neural articulated radiance fields, yet their
generalization ability and controllability lag behind parametric human models,
i.e., they do not perform well when generalizing to novel pose/shape and are
not part controllable. To solve these problems, we propose VeRi3D, a generative
human vertex-based radiance field parameterized by vertices of the parametric
human template, SMPL. We map each 3D point to the local coordinate system
defined on its neighboring vertices, and use the corresponding vertex feature
and local coordinates for mapping it to color and density values. We
demonstrate that our simple approach allows for generating photorealistic human
images with free control over camera pose, human pose, shape, as well as
enabling part-level editing.

提出了 VeRi3D，一种基于顶点的生成型人体辐射场，通过使用 SMPL 的参数人体模板来实现顶点参数化，从而解决了学习人体生成模型的泛化能力和可控性问题，能够自由地控制相机姿态、人体姿态、形状，以及进行部分级别的编辑，实现了生成逼真的人体图像。