Traditional 3D garment creation is labor-intensive, involving sketching,
modeling, UV mapping, and texturing, which are time-consuming and costly.
Recent advances in diffusion-based generative models have enabled new
possibilities for 3D garment generation from text prompts, images, and videos.
However, existing methods either suffer from inconsistencies among multi-view
images or require additional processes to separate cloth from the underlying
human model. In this paper, we propose GarmentDreamer, a novel method that
leverages 3D Gaussian Splatting (GS) as guidance to generate wearable,
simulation-ready 3D garment meshes from text prompts. In contrast to using
multi-view images directly predicted by generative models as guidance, our 3DGS
guidance ensures consistent optimization in both garment deformation and
texture synthesis. Our method introduces a novel garment augmentation module,
guided by normal and RGBA information, and employs implicit Neural Texture
Fields (NeTF) combined with Score Distillation Sampling (SDS) to generate
diverse geometric and texture details. We validate the effectiveness of our
approach through comprehensive qualitative and quantitative experiments,
showcasing the superior performance of GarmentDreamer over state-of-the-art
alternatives. Our project page is available at:
this https URL

本研究提出了一种名为 GarmentDreamer 的新型方法，它利用 3D 高斯喷雾（GS）作为指导，从文本提示中生成适用于仿真的 3D 服装网格。与使用由生成模型直接预测的多视图图像作为指导相比，我们的 3DGS 指导确保了服装变形和纹理合成的一致优化。通过全面的定性和定量实验证明了我们方法的有效性，并展示了 GarmentDreamer 相对于现有最先进方法的卓越性能。