East China Normal University（华东师范大学）；Xiaohongshu Inc.（小红书）；KLATASDS, MOE, China（中国教育部统计与数据科学重点实验室）；The Chinese University of Hong Kong（香港中文大学）；Zhejiang University（浙江大学）；Fudan University（复旦大学）；University of Oxford（牛津大学）；Shanghai Jiao Tong University（上海交通大学）；University of Science and Technology of China（中国科学技术大学）；Nanjing University（南京大学）
While real-world applications increasingly demand intricate scene manipulation, existing instruction-guided image editing benchmarks often oversimplify task complexity and lack comprehensive, fine-grained instructions. To bridge this gap, we introduce CompBench, a large-scale benchmark specifically designed for complex instruction-guided image editing. CompBench features challenging editing scenarios that incorporate fine-grained instruction following, spatial and contextual reasoning, thereby enabling comprehensive evaluation of image editing models' precise manipulation capabilities. To construct CompBench, we propose an MLLM-human collaborative framework with tailored task pipelines. Furthermore, we propose an instruction decoupling strategy that disentangles editing intents into four key dimensions: location, appearance, dynamics, and objects, ensuring closer alignment between instructions and complex editing requirements. Extensive evaluations reveal that CompBench exposes fundamental limitations of current image editing models and provides critical insights for the development of next-generation instruction-guided image editing systems. Our project page is available at https://comp-bench.github.io/.
Southeast University（东南大学）；Max Planck Institute for Informatics（马克斯·普朗克信息研究所）；Shanghai Jiao Tong University（上海交通大学）；StepFun；University of California, Berkeley（加州大学伯克利分校）；University of California, Merced（加州大学默塞德分校）
ByteMorph: Benchmarking Instruction-Guided Image Editing with Non-Rigid Motions
ByteDance Seed（字节跳动种子）；University of Southern California（南加州大学）；University of Tokyo（东京大学）；University of California Berkeley（加州大学伯克利分校）；Stanford University（斯坦福大学）；University of California Los Angeles（加州大学洛杉矶分校）
WEAVE: Unleashing and Benchmarking the In-context Interleaved Comprehension and Generation
National University of Singapore（新加坡国立大学）；Nanyang Technological University（南洋理工大学）；University of Maryland, College Park（马里兰大学学院公园分校）；Zhejiang University（浙江大学）
LMM4Edit: Benchmarking and Evaluating Multimodal Image Editing with LMMs
Institute of Image Communication and Network Engineering, Shanghai JiaoTong University（上海交通大学图像通信与网络工程研究所）；University of Electronic and Science Technology of China（电子科技大学）；Nanyang Technological University（南洋理工大学）
InEdit-Bench: Benchmarking Intermediate Logical Pathways for Intelligent Image Editing Models
State Key Laboratory of Optical Field Manipulation Science and Technology, Institute of Optics and Electronics, Chinese Academy of Sciences（光学场操控科学与技术国家重点实验室，光学电子研究所，中国科学院）；National Laboratory on Adaptive Optics（自适应光学国家实验室）；University of Chinese Academy of Sciences（中国科学院大学）
IVEBench: Modern Benchmark Suite for Instruction-Guided Video Editing Assessment
Zhejiang University（浙江大学）；Tencent Youtu Lab（腾讯优图实验室）；Shanghai Jiao Tong University（上海交通大学）；University of Auckland（奥克兰大学）；National University of Singapore（新加坡国立大学）
EditScore: Unlocking Online RL for Image Editing via High-Fidelity Reward Modeling
University of Science and Technology of China（中国科学技术大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Zhejiang University（浙江大学）
PICABench: How Far Are We from Physically Realistic Image Editing?
Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）；CUHK MMLab（香港中文大学多媒体实验室）；Krea AI；Beihang University（北京航空航天大学）；Tongyi Lab（通义实验室）；USTC（中国科学技术大学）；The University of Hong Kong（香港大学）
MICON-Bench: Benchmarking and Enhancing Multi-Image Context Image Generation in Unified Multimodal Models
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（多媒体可信感知与高效计算重点实验室，中国教育部，厦门大学）；Zhongguancun Academy, Beijing, China（中关村学院，北京，中国）
The University of Hong Kong（香港大学）；JD Explore Academy（京东探索研究院）；Tsinghua University（清华大学）；The Hong Kong University of Science and Technology（香港科技大学）；The Chinese University of Hong Kong（香港中文大学）
Polyp-E: Benchmarking the Robustness of Deep Segmentation Models via Polyp Editing
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；The Fifth Medical Center, Chinese PLA General Hospital（中国人民解放军总医院第五医学中心）；Children’s Hospital of Zhejiang University School of Medicine（浙江大学医学院附属儿童医院）
Alibaba Group（阿里巴巴集团）；Zhejiang University（浙江大学）；Inst. of Comput. Tech., CAS（中国科学院计算技术研究所）
Recent studies have shown that higher accuracy on ImageNet usually leads to better robustness against different corruptions. Therefore, in this paper, instead of following the traditional research paradigm that investigates new out-of-distribution corruptions or perturbations deep models may encounter, we conduct model debugging in in-distribution data to explore which object attributes a model may be sensitive to. To achieve this goal, we create a toolkit for object editing with controls of backgrounds, sizes, positions, and directions, and create a rigorous benchmark named ImageNet-E(diting)…
In image editing, the most common task is pasting objects from one image to the other and then eventually adjusting the manifestation of the foreground object with the background object. This task is called image compositing. But image compositing is a challenging problem that requires professional editing skills and a considerable amount of time. Not only these professionals are expensive to hire, but the tools (like Adobe Photoshop) used for doing such tasks are also expensive to purchase making the overall task of image compositing difficult for people without this skillset. In this work, w…
