Despite remarkable progress in text-guided image editing, generative models frequently fail to preserve visual object consistency, defined as the preservation of a subject's key attributes throughout the editing process. We address this limitation through three contributions.
PrismGPT is a Vision‑Language Model that generates structured, region‑aware photo‑editing plans from a single image, without relying on commercial black‑box tools. It learns to diagnose aesthetic issues globally and locally while predicting precise editing parameters, using proxy‑guided learning with operation decomposition and region‑aware aesthetic ranking to bootstrap the model. A competence‑based dynamic scheduler shifts training focus from proxy tasks to the main editing task as skills improve, and all reasoning traces for fine‑tuning are self‑synthesized by the model itself. Experiments on MIT‑Adobe FiveK and a new professionally retouched benchmark, SPIRE, show PrismGPT achieves state‑of‑the‑art results using only about 6% of the training data required by previous methods.
By Ke Zhao, Hue Nguyen, Abhijith Punnappurath, Zhongling Wang, Iqbal Mohomed, Michael S. Brown
Recent breakthroughs in instruction-based image editing have captured significant attention, as models are now capable of handling real-world editing demands with the practicality required by everyday users. However, editing models trained primarily for single-turn edits often break down in multi-turn editing--the natural interactive setting where a user iteratively refines an image based on the model's own previous outputs.
arXiv:2608.22780v1 Announce Type: new
Abstract: Reinforcement learning (RL) enables direct preference optimization for image editing through editing-specific rewards, which remain less developed due...
By Qichao Ma, Jikang Cheng, Ling Liang, Zhaofei Yu, Tiejun Huang, Renye Yan
arXiv:2603. 03143v2 Announce Type: replace-cross Abstract: Leveraging the priors of 2D diffusion models for 3D editing has emerged as a promising paradigm.
By Jiyuan Wang, Chunyu Lin, Lei Sun, Zhi Cao, Yuyang Yin, Lang Nie, Zhenlong Yuan, Xiangxiang Chu, Yunchao Wei, Kang Liao, Guosheng Lin
RefVideo-6M is a new large-scale reference-guided editing dataset that includes 5 million video editing samples and 1 million image editing samples, each paired with about 6 million visual references. The dataset is constructed to avoid artifacts by using real, artifact‑free videos as targets and filtering input conditions with multiple editing experts, thereby providing reliable supervision. It enables models to learn fine‑grained visual correspondence beyond text‑only instructions and supports the training of a reference‑guided video editing model, Ref‑MoT, which shows improved visual quality, controllability, and reference consistency.
By Bojia Zi, Xiaoyan Yang, Yu Zhou, Ruijie Sun, Lihan Zhang, Bin Liang, Kam-Fai Wong, Haibin Huang, Chi Zhang, Xuelong Li