OpenCIR: Conditional Image Repainting With Open Condition Mixture

Shuchen Weng, Xiaocheng Gong, Haojie Zheng, Xinlong Wang, Si Li, Boxin Shi

Abstract: In this paper, we introduce OpenCIR, a fully-functional Conditional Image Repainting (CIR) model designed for local image editing. Given an image and a combination of conditions related to geometry, texture, and color, CIR models are required to repaint instances and seamlessly composite them with the original images. Previous CIR models suffer from limited object categories, restricted condition modalities, and demanded geometry precision. In contrast, leveraging the generative priors from pre-trained models, OpenCIR could repaint open object categories. Equipped with redesigned condition injection modules and the condition extension strategy, OpenCIR is able to understand open condition modalities. Adopting the contour refinement strategy, OpenCIR allows users to specify instances with open geometry precision. In addition, we contribute the Open-CIR dataset, which includes detailed annotations, tailored for the comprehensive training and evaluation of the OpenCIR model. Extensive experiments demonstrate that OpenCIR outperforms relevant state-of-the-art methods, achieving superior visual quality, and more favorable results by human evaluators.

External IDs:dblp:journals/pami/WengGZWLS25