  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.
