PaintHuman: Towards High-fidelity Text-to-3D Human Texturing via
  Denoised Score Distillation
Jianhui Yu Hao Zhu Liming Jiang Chen Change Loy Weidong Cai Wayne Wu
Recent advances in zero-shot text-to-3D human generation, which employ the
human model prior (eg, SMPL) or Score Distillation Sampling (SDS) with
pre-trained text-to-image diffusion models, have been groundbreaking. However,
SDS may provide inaccurate gradient directions under the weak diffusion
guidance, as it tends to produce over-smoothed results and generate body
textures that are inconsistent with the detailed mesh geometry. Therefore,
directly leverage existing strategies for high-fidelity text-to-3D human
texturing is challenging. In this work, we propose a model called PaintHuman to
addresses the challenges from two aspects. We first propose a novel score
function, Denoised Score Distillation (DSD), which directly modifies the SDS by
introducing negative gradient components to iteratively correct the gradient
direction and generate high-quality textures. In addition, we use the depth map
as a geometric guidance to ensure the texture is semantically aligned to human
mesh surfaces. To guarantee the quality of rendered results, we employ
geometry-aware networks to predict surface materials and render realistic human
textures. Extensive experiments, benchmarked against state-of-the-art methods,
validate the efficacy of our approach.