MotionEdit: Benchmarking and Learning Motion-Centric Image Editing
Tencent AI（腾讯AI）；University of California, Los Angeles（加州大学洛杉矶分校）
Yixin Wan, Lei Ke, Wenhao Yu, Kai-Wei Chang, Dong Yu
备注：
Technical Report. We propose MotionEdit, a dataset and benchmark for motion-centric image editing. We also introduce MotionNFT, a reward training framework to improve existing models with motion-aware guidance. Github: https://github.com/elainew728/motion-edit/
We introduce MotionEdit, a novel dataset for motion-centric image editing-the task of modifying subject actions and interactions while preserving identity, structure, and physical plausibility. Unlike existing image editing datasets that focus on static appearance changes or contain only sparse, low-quality motion edits, MotionEdit provides high-fidelity image pairs depicting realistic motion transformations extracted and verified from continuous videos. This new task is not only scientifically challenging but also practically significant, powering downstream applications such as frame-controlled video synthesis and animation.
To evaluate model performance on the novel task, we introduce MotionEdit-Bench, a benchmark that challenges models on motion-centric edits and measures model performance with generative, discriminative, and preference-based metrics. Benchmark results reveal that motion editing remains highly challenging for existing state-of-the-art diffusion-based editing models. To address this gap, we propose MotionNFT (Motion-guided Negative-aware Fine Tuning), a post-training framework that computes motion alignment rewards based on how well the motion flow between input and model-edited images matches the ground-truth motion, guiding models toward accurate motion transformations. Extensive experiments on FLUX.1 Kontext and Qwen-Image-Edit show that MotionNFT consistently improves editing quality and motion fidelity of both base models on the motion editing task without sacrificing general editing ability, demonstrating its effectiveness. Our code is at https://github.com/elainew728/motion-edit/.
MotionEditor: Editing Video Motion via Content-Aware Diffusion
Fudan University（复旦大学）；Shanghai Collaborative Innovation Center of Intelligent Visual Computing（上海智能视觉计算协同创新中心）；Microsoft Research Asia（微软亚洲研究院）；Carnegie Mellon University（卡内基梅隆大学）；Huya Inc.（虎牙公司）
MotionFollower: Editing Video Motion via Lightweight Score-Guided Diffusion
Fudan University（复旦大学）；Shanghai Collaborative Innovation Center of Intelligent Visual Computing（上海智能视觉计算协同创新中心）；Microsoft Research Asia（微软亚洲研究院）；Carnegie Mellon University（卡内基梅隆大学）；Huya Inc.（虎牙公司）
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）
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）
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）
Beyond Coherence: Benchmarking Professional Editing-Technique Execution in Multi-Shot Audio-Video Generation
Shanghai Jiao Tong University（上海交通大学）；Alibaba Group（阿里巴巴集团）；Fudan University（复旦大学）；UT Austin（德克萨斯大学奥斯汀分校）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）
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）
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）
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）
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）
RFM-Editing: Rectified Flow Matching for Text-guided Audio Editing
Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey, United Kingdom（视觉、语音和信号处理中心（CVSSP），英国萨里大学）；College of Information and Electrical Engineering, China Agricultural University, China（信息与电气工程学院，中国农业大学）；Fisheries College, Ocean University of China, China（海洋大学渔业学院，中国）
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）
Towards Emotionally Consistent Text-Based Speech Editing: Introducing EmoCorrector and The ECD-TSE Dataset
Johns Hopkins University（约翰霍普金斯大学）；Carnegie Mellon University（卡内基梅隆大学）；The Chinese University of Hong Kong（香港中文大学）；National University of Singapore（新加坡国立大学）；Inner Mongolia University（内蒙古大学）
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）
Agents Don't Just Agree, They Remember: Benchmarking Persistent Sycophancy in Self-Improving Personal Agents
City University of Hong Kong（香港城市大学）；ByteDance（字节跳动）；Yale University（耶鲁大学）；Fudan University（复旦大学）；Hong Kong Baptist University（香港浸会大学）；Dalian University of Technology（大连理工大学）
This paper describes the corpus of sockpuppet cases we gathered from Wikipedia. A sockpuppet is an online user account created with a fake identity for the purpose of covering abusive behavior and/or subverting the editing regulation process. We used a semi-automated method for crawling and curating a dataset of real sockpuppet investigation cases. To the best of our knowledge, this is the first corpus available on real-world deceptive writing. We describe the process for crawling the data and some preliminary results that can be used as baseline for benchmarking research. The dataset will be …
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…
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）
