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.

Overview of the proposed HumanGaussian Framework. We generate high-quality 3D humans from text prompts with the neural representation of 3D Gaussian Splatting (3DGS). In Structure-Aware SDS, we start from the SMPL-X prior to densely sample Gaussians on the human mesh surface as initial center positions. Then, a Texture-Structure Joint Model is trained to simultaneously denoise the image x and depth d conditioned on pose skeleton p. Based on this, we design a dual-branch SDS to jointly optimize human appearance and geometry, where the 3DGS density is adaptively controlled by distilling from both the RGB and depth space. In Annealed Negative Prompt Guidance, we use the cleaner classifier score with an annealed negative score to regularize the stochastic SDS gradient of high variance. The floating artifacts are further eliminated based on Gaussian size in a prune-only phase to enhance generation smoothness.

Visual Comparisons with Text-to-3D and 3D Human Models. We compare with recent state-of-the-art baselines on five different prompts, each showing two camera views. Note that the textural unrealism and blurriness are highlighted with yellow arrows; the geometric artifacts are highlighted with green rectangles. Please kindly zoom in for best view and refer to demo video for more results.

Ablation Studies on HumanGaussian Module Design. We present generation results of the human frontal view under five ablation settings for better visualization and comparisons: (A) baseline; (B) +SMPL-X, Pose-Cond.; (C) +Neg. Guidance, CFG=7.5; (D) +Dual-Branch SDS; (E) +Size-based Prune. The detailed ablation setting designs and result analysis are elaborated in Sec.4.3.

Though the HumanGaussian framework is trained on a single body pose at the training stage, it can be animated with unseen pose sequences in a zero-shot manner, i.e., we can use a sequence of SMPL-X pose parameters to animate the pre-trained avatars w/o further finetuning.