arXiv:2607. 21318v1 Announce Type: cross Abstract: Replacing an object with one that differs in category or shape requires complete source removal, natural target formation unconstrained by the source silhouette, and preservation of unrelated content.
By Jian Zhang, Zhijun Zhang
Replacing an object with one that differs in category or shape requires complete source removal, natural target formation unconstrained by the source silhouette, and preservation of unrelated content. Existing training-free editors either localize edits from terminal predictions under source and target prompts or preserve unrelated content through spatially unselective source-feature reuse without explicit region discovery.
SR-Edit is a new image editing framework that uses iterative self‑refinement to improve fidelity. At each step it extracts precise, self‑consistent region separations from the model’s predictions and then enforces preservation in non‑edit areas with correction updates that stay aligned with the original sampling dynamics. Experiments show that SR‑Edit delivers better preservation and overall image quality than existing editing techniques.
By Andong Wang, Zehua Chen, Yuxuan Jiang, Jun Zhu
VibeEdit is a new image‑editing tool that lets users draw spatial marks and add brief notes directly on an image to create a canvas instruction. These instructions guide the model to perform tasks such as adding, removing, replacing, or moving objects, as well as modifying attributes, without needing a separate text prompt. The system is trained on 1.55 million annotated edit pairs and uses a layer‑decoupled conditioning approach, achieving higher VLM rubric scores and PSNR on a benchmark that tests target selection among similar objects.
By Jinjing Zhao, Fangyun Wei, Yitong Wang, Xiuyu Wu, Yunuo Chen, Yang Yue, Sirui Zhang, Wenbo Wang, Hongyang Zhang, Dong Chen, Yan Lu, Chang Xu
The paper introduces StruPO, a structure‑aware prompt editor that uses a calibrated scene graph to guide prompt modifications while preserving requested scene attributes. By aligning token edits with graph‑based structural feedback, StruPO learns a reusable editing policy that improves faithfulness and relational fidelity across multiple generation backbones. Experiments on diverse test sets and human studies show consistent gains in image quality and preference, especially for complex relations.
By Wentao Ye, Yali Ye, Zhiqing Xiao, Ru Peng, Xinpeng Ti, Liyao Li, Zhanming Shen, Peng Lu, Jie Zhang, Haobo Wang
arXiv:2606. 14125v1 Announce Type: cross Abstract: Inversion-based image editing offers flexible and training-free control but still struggles with inversion accuracy and the trade-off between editing fidelity and background preservation.
By Zheyuan Zhan, Hongchen Li, Can Wang, Yinfei Ma, Mingzhen Huang, Ruoshi Bai, Jiawei Chen, Siwei Lyu, Defang Chen