  Realistic 3D human generation from text prompts is a desirable yet
challenging task. Existing methods optimize 3D representations like mesh or
neural fields via score distillation sampling (SDS), which suffers from
inadequate fine details or excessive training time. In this paper, we propose
an efficient yet effective framework, HumanGaussian, that generates
high-quality 3D humans with fine-grained geometry and realistic appearance. Our
key insight is that 3D Gaussian Splatting is an efficient renderer with
periodic Gaussian shrinkage or growing, where such adaptive density control can
be naturally guided by intrinsic human structures. Specifically, 1) we first
propose a Structure-Aware SDS that simultaneously optimizes human appearance
and geometry. The multi-modal score function from both RGB and depth space is
leveraged to distill the Gaussian densification and pruning process. 2)
Moreover, we devise an Annealed Negative Prompt Guidance by decomposing SDS
into a noisier generative score and a cleaner classifier score, which well
addresses the over-saturation issue. The floating artifacts are further
eliminated based on Gaussian size in a prune-only phase to enhance generation
smoothness. Extensive experiments demonstrate the superior efficiency and
competitive quality of our framework, rendering vivid 3D humans under diverse
scenarios. Project Page: https://alvinliu0.github.io/projects/HumanGaussian
