  Inverse graphics aims to recover 3D models from 2D observations. Utilizing
differentiable rendering, recent 3D-aware generative models have shown
impressive results of rigid object generation using 2D images. However, it
remains challenging to generate articulated objects, like human bodies, due to
their complexity and diversity in poses and appearances. In this work, we
propose, EVA3D, an unconditional 3D human generative model learned from 2D
image collections only. EVA3D can sample 3D humans with detailed geometry and
render high-quality images (up to 512x256) without bells and whistles (e.g.
super resolution). At the core of EVA3D is a compositional human NeRF
representation, which divides the human body into local parts. Each part is
represented by an individual volume. This compositional representation enables
1) inherent human priors, 2) adaptive allocation of network parameters, 3)
efficient training and rendering. Moreover, to accommodate for the
characteristics of sparse 2D human image collections (e.g. imbalanced pose
distribution), we propose a pose-guided sampling strategy for better GAN
learning. Extensive experiments validate that EVA3D achieves state-of-the-art
3D human generation performance regarding both geometry and texture quality.
Notably, EVA3D demonstrates great potential and scalability to
"inverse-graphics" diverse human bodies with a clean framework.
