ControlEdit: A MultiModal Local Clothing Image Editing Method

TL;DR

ControlEdit introduces a multimodal-guided local inpainting method for clothing image editing, leveraging self-supervised learning and latent diffusion to enable precise, style-consistent modifications with natural transitions.

Contribution

It proposes a novel multimodal clothing editing approach using self-supervised learning and latent diffusion, improving editing quality and control over non-edited areas.

Findings

Outperforms baseline algorithms in qualitative evaluations.

Achieves consistent clothing style before and after editing.

Enables natural transition boundaries in edited images.

Abstract

Multimodal clothing image editing refers to the precise adjustment and modification of clothing images using data such as textual descriptions and visual images as control conditions, which effectively improves the work efficiency of designers and reduces the threshold for user design. In this paper, we propose a new image editing method ControlEdit, which transfers clothing image editing to multimodal-guided local inpainting of clothing images. We address the difficulty of collecting real image datasets by leveraging the self-supervised learning approach. Based on this learning approach, we extend the channels of the feature extraction network to ensure consistent clothing image style before and after editing, and we design an inverse latent loss function to achieve soft control over the content of non-edited areas. In addition, we adopt Blended Latent Diffusion as the sampling method to make the editing boundaries transition naturally and enforce consistency of non-edited area content. Extensive experiments demonstrate that ControlEdit surpasses baseline algorithms in both qualitative and quantitative evaluations.