In this paper, we tackle a new and challenging problem of text-driven generation of 3D garments with high-quality textures. We propose "WordRobe", a novel framework for the generation of unposed & textured 3D garment meshes from user-friendly text prompts. We achieve this by first learning a latent representation of 3D garments using a novel coarse-to-fine training strategy and a loss for latent disentanglement, promoting better latent interpolation. Subsequently, we align the garment latent space to the CLIP embedding space in a weakly supervised manner, enabling text-driven 3D garment generation and editing. For appearance modeling, we leverage the zero-shot generation capability of ControlNet to synthesize view-consistent texture maps in a single feed-forward inference step, thereby drastically decreasing the generation time as compared to existing methods. We demonstrate superior performance over current SOTAs for learning 3D garment latent space, garment interpolation, and text-driven texture synthesis, supported by quantitative evaluation and qualitative user study. The unposed 3D garment meshes generated using WordRobe can be directly fed to standard cloth simulation & animation pipelines without any post-processing.

翻译：本文针对文本驱动的带高质量纹理3D服装生成这一全新且具有挑战性的问题展开研究。我们提出"WordRobe"——一个创新框架，能够从用户友好的文本提示中生成无姿态且带有纹理的3D服装网格。为实现这一目标，我们首先通过新颖的由粗到细训练策略和促进潜在解耦的损失函数，学习3D服装的潜在表征，从而改善潜在插值效果。随后，我们以弱监督方式将服装潜在空间与CLIP嵌入空间对齐，实现文本驱动的3D服装生成与编辑。在外观建模方面，我们利用ControlNet的零样本生成能力，通过单次前向推理步骤合成视角一致的纹理贴图，从而较现有方法显著缩短生成时间。通过定量评估与定性用户研究，我们证明该方法在3D服装潜在空间学习、服装插值及文本驱动纹理合成方面均优于现有最先进技术。使用WordRobe生成的无姿态3D服装网格可直接输入标准布料仿真与动画流程，无需任何后处理。