  Recent 3D human generative models have achieved remarkable progress by
learning 3D-aware GANs from 2D images. However, existing 3D human generative
methods model humans in a compact 1D latent space, ignoring the articulated
structure and semantics of human body topology. In this paper, we explore more
expressive and higher-dimensional latent space for 3D human modeling and
propose StructLDM, a diffusion-based unconditional 3D human generative model,
which is learned from 2D images. StructLDM solves the challenges imposed due to
the high-dimensional growth of latent space with three key designs: 1) A
semantic structured latent space defined on the dense surface manifold of a
statistical human body template. 2) A structured 3D-aware auto-decoder that
factorizes the global latent space into several semantic body parts
parameterized by a set of conditional structured local NeRFs anchored to the
body template, which embeds the properties learned from the 2D training data
and can be decoded to render view-consistent humans under different poses and
clothing styles. 3) A structured latent diffusion model for generative human
appearance sampling. Extensive experiments validate StructLDM's
state-of-the-art generation performance and illustrate the expressiveness of
the structured latent space over the well-adopted 1D latent space. Notably,
StructLDM enables different levels of controllable 3D human generation and
editing, including pose/view/shape control, and high-level tasks including
compositional generations, part-aware clothing editing, 3D virtual try-on, etc.
Our project page is at: https://taohuumd.github.io/projects/StructLDM/.
