  While NeRF-based 3D-aware image generation methods enable viewpoint control,
limitations still remain to be adopted to various 3D applications. Due to their
view-dependent and light-entangled volume representation, the 3D geometry
presents unrealistic quality and the color should be re-rendered for every
desired viewpoint. To broaden the 3D applicability from 3D-aware image
generation to 3D-controllable object generation, we propose the factorized
representations which are view-independent and light-disentangled, and training
schemes with randomly sampled light conditions. We demonstrate the superiority
of our method by visualizing factorized representations, re-lighted images, and
albedo-textured meshes. In addition, we show that our approach improves the
quality of the generated geometry via visualization and quantitative
comparison. To the best of our knowledge, this is the first work that extracts
albedo-textured meshes with unposed 2D images without any additional labels or
assumptions.
