  Despite significant advances in large-scale text-to-image models, achieving
hyper-realistic human image generation remains a desirable yet unsolved task.
Existing models like Stable Diffusion and DALL-E 2 tend to generate human
images with incoherent parts or unnatural poses. To tackle these challenges,
our key insight is that human image is inherently structural over multiple
granularities, from the coarse-level body skeleton to fine-grained spatial
geometry. Therefore, capturing such correlations between the explicit
appearance and latent structure in one model is essential to generate coherent
and natural human images. To this end, we propose a unified framework,
HyperHuman, that generates in-the-wild human images of high realism and diverse
layouts. Specifically, 1) we first build a large-scale human-centric dataset,
named HumanVerse, which consists of 340M images with comprehensive annotations
like human pose, depth, and surface normal. 2) Next, we propose a Latent
Structural Diffusion Model that simultaneously denoises the depth and surface
normal along with the synthesized RGB image. Our model enforces the joint
learning of image appearance, spatial relationship, and geometry in a unified
network, where each branch in the model complements to each other with both
structural awareness and textural richness. 3) Finally, to further boost the
visual quality, we propose a Structure-Guided Refiner to compose the predicted
conditions for more detailed generation of higher resolution. Extensive
experiments demonstrate that our framework yields the state-of-the-art
performance, generating hyper-realistic human images under diverse scenarios.
Project Page: https://snap-research.github.io/HyperHuman/
