We introduce GarmentCrafter, a new approach that enables non-professional users to create and modify 3D garments from a single-view image. While recent advances in image generation have facilitated 2D garment design, creating and editing 3D garments remains challenging for non-professional users. Existing methods for single-view 3D reconstruction often rely on pre-trained generative models to synthesize novel views conditioning on the reference image and camera pose, yet they lack cross-view consistency, failing to capture the internal relationships across different views. In this paper, we tackle this challenge through progressive depth prediction and image warping to approximate novel views. Subsequently, we train a multi-view diffusion model to complete occluded and unknown clothing regions, informed by the evolving camera pose. By jointly inferring RGB and depth, GarmentCrafter enforces inter-view coherence and reconstructs precise geometries and fine details. Extensive experiments demonstrate that our method achieves superior visual fidelity and inter-view coherence compared to state-of-the-art single-view 3D garment reconstruction methods.

本研究解决了非专业用户在单视图图像中创建和编辑3D服装的难题。通过逐步深度预测和图像扭曲的方法，提出了一种新的3D服装重建技术，保证了视图间的一致性和细节的精确重构。实验表明，GarmentCrafter在视觉保真度和视图一致性方面优于现有的3D服装重建方法。

GarmentCrafter: 逐步新视图合成用于单视图3D服装重建与编辑