The increasing prominence of e-commerce has underscored the importance of
Virtual Try-On (VTON). However, previous studies predominantly focus on the 2D
realm and rely heavily on extensive data for training. Research on 3D VTON
primarily centers on garment-body shape compatibility, a topic extensively
covered in 2D VTON. Thanks to advances in 3D scene editing, a 2D diffusion
model has now been adapted for 3D editing via multi-viewpoint editing. In this
work, we propose GaussianVTON, an innovative 3D VTON pipeline integrating
Gaussian Splatting (GS) editing with 2D VTON. To facilitate a seamless
transition from 2D to 3D VTON, we propose, for the first time, the use of only
images as editing prompts for 3D editing. To further address issues, e.g., face
blurring, garment inaccuracy, and degraded viewpoint quality during editing, we
devise a three-stage refinement strategy to gradually mitigate potential
issues. Furthermore, we introduce a new editing strategy termed Edit Recall
Reconstruction (ERR) to tackle the limitations of previous editing strategies
in leading to complex geometric changes. Our comprehensive experiments
demonstrate the superiority of GaussianVTON, offering a novel perspective on 3D
VTON while also establishing a novel starting point for image-prompting 3D
scene editing.

本研究提出了一种创新的 3D VTON 管道，将高斯喷洒（GS）编辑与 2D VTON 相结合，并首次提出仅使用图像作为 3D 编辑的编辑提示，同时采用三阶段细化策略逐渐减轻潜在问题，并引入一种名为 ERR 的新的编辑策略来解决以往编辑策略在引起复杂几何变化方面的局限性。 实验结果表明 GaussianVTON 优越，为 3D VTON 提供了新的视角，也为基于图像提示的 3D 场景编辑建立了新的起点。