JAVEDIT: Joint Audio-Visual Instruction-Guided Video Editing with Agentic Data Curation
Zhejiang University（浙江大学）；Tencent Youtu Lab（腾讯优图实验室）；Nanjing University（南京大学）；University of Auckland（奥克兰大学）；Fudan University（复旦大学）；National University of Singapore（新加坡国立大学）
While instruction-based video editing has seen significant progress, joint audio-visual editing remains constrained by the absence of dedicated datasets and benchmarks. To bridge this gap, we present JAVEdit-100k, the first large-scale, high-quality dataset tailored for instruction-guided joint audio-visual editing. Focusing on human-centric videos, JAVEdit-100k comprises approximately 100K editing triplets spanning five distinct categories, including subject editing and speech editing. This dataset is rigorously constructed via four meticulously designed generation pipelines, seamlessly paired with an agent-in-the-loop quality control mechanism. Furthermore, to address the lack of standardized evaluation within the field, we introduce JAVEditBench, a comprehensive benchmark featuring curated source videos and human-aligned instructions across all editing categories. Finally, we propose JAVEdit, a pioneering baseline model for instruction-guided joint audio-visual editing. Experiments show that \model\ outperforms all baselines on five of six evaluation metrics.
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）
CV-Arena: An Open Benchmark for Instructional Computer Vision Problem Solving with Human-AI Collaborative Preferences
Texas A&M University（德克萨斯A&M大学）；Worcester Polytechnic Institute（沃斯特理工大学）；Tohoku University（东北大学）；Georgia Institute of Technology（佐治亚理工学院）；NVIDIA（英伟达）；UCSB（加州大学圣塔芭芭拉分校）；UC Merced（加州大学默塞德分校）
DisciplineGen-1M: A Large-Scale Dataset for Multidisciplinary Visual Generation and Editing
Shanghai Jiao Tong University（上海交通大学）；South China University of Technology（华南理工大学）；Xiamen University（厦门大学）；University of Science and Technology of China（中国科学技术大学）
IBM T. J. Watson Research Center（IBM托马斯·J·沃森研究中心）；IBM iX
The production of sports highlight packages summarizing a game's most exciting moments is an essential task for broadcast media. Yet, it requires labor-intensive video editing. We propose a novel approach for auto-curating sports highlights, and use it to create a real-world system for the editorial aid of golf highlight reels. Our method fuses information from the players' reactions (action recognition such as high-fives and fist pumps), spectators (crowd cheering), and commentator (tone of the voice and word analysis) to determine the most interesting moments of a game. We accurately identif…
MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；Qatar Computer Research Institute（卡塔尔计算机研究所）
Street maps are a crucial data source that help to inform a wide range of decisions, from navigating a city to disaster relief and urban planning. However, in many parts of the world, street maps are incomplete or lag behind new construction. Editing maps today involves a tedious process of manually tracing and annotating roads, buildings, and other map features. Over the past decade, many automatic map inference systems have been proposed to automatically extract street map data from satellite imagery, aerial imagery, and GPS trajectory datasets. However, automatic map inference has failed to…
Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling
Tsinghua University（清华大学）；Nanyang Technological University（南洋理工大学）；University of Hong Kong（香港大学）；National University of Singapore（新加坡国立大学）；University of Waterloo（滑铁卢大学）；StepFun；MiroMind；Baidu（百度）；Fudan University（复旦大学）；Hong Kong University of Science and Technology（香港科技大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；LMMs-Lab（LMMs实验室）
