Deep learning-based image enhancement methods show significant advantages in reducing noise and improving visibility in low-light conditions. These methods are typically based on one-to-one mapping, where the model learns a direct transformation from low light to specific enhanced images. Therefore, these methods are inflexible as they do not allow highly personalized mapping, even though an individual's lighting preferences are inherently personalized. To overcome these limitations, we propose a new light enhancement task and a new framework that provides customized lighting control through prompt-driven, semantic-level, and quantitative brightness adjustments. The framework begins by leveraging a Large Language Model (LLM) to understand natural language prompts, enabling it to identify target objects for brightness adjustments. To localize these target objects, the Retinex-based Reasoning Segment (RRS) module generates precise target localization masks using reflection images. Subsequently, the Text-based Brightness Controllable (TBC) module adjusts brightness levels based on the generated illumination map. Finally, an Adaptive Contextual Compensation (ACC) module integrates multi-modal inputs and controls a conditional diffusion model to adjust the lighting, ensuring seamless and precise enhancements accurately. Experimental results on benchmark datasets demonstrate our framework's superior performance at increasing visibility, maintaining natural color balance, and amplifying fine details without creating artifacts. Furthermore, its robust generalization capabilities enable complex semantic-level lighting adjustments in diverse open-world environments through natural language interactions.
Variable Aperture Bokeh Rendering via Customized Focal Plane Guidance
School of Computer and Information Engineering, Jiangxi Normal University（江西师范大学计算机与信息工程学院）；School of Digital Industry, Jiangxi Normal University（江西师范大学数字产业学院）；School of Computer Science, National University of Defense Technology（国防科技大学计算机学院）
In this paper, we propose a novel framework for music-driven dance motion synthesis with controllable key pose constraint. In contrast to methods that generate dance motion sequences only based on music without any other controllable conditions, this work targets on synthesizing high-quality dance motion driven by music as well as customized poses performed by users. Our model involves two single-modal transformer encoders for music and motion representations and a cross-modal transformer decoder for dance motions generation. The cross-modal transformer decoder achieves the capability of synth…
QCAgent: An agentic framework for quality-controllable pathology report generation from whole slide image
School of Biomedical Engineering, Division of Life Sciences；Medicine, University of Science；Technology of China, Hefei, Anhui, 230026, P.R. China Center for Medical Imaging, Robotics, Analytic Computing \& Learning (MIRACLE), Suzhou Institute for Advance Research, USTC, 215123, P.R. China Chinese PLA General Hospital Ninth Medical Center, China Hong Kong University of Science
Tencent AI Lab（腾讯人工智能实验室）；Tsinghua University（清华大学）；Meituan（美团）
In this paper, we investigate a novel and challenging task, namely controllable video captioning with an exemplar sentence. Formally, given a video and a syntactically valid exemplar sentence, the task aims to generate one caption which not only describes the semantic contents of the video, but also follows the syntactic form of the given exemplar sentence. In order to tackle such an exemplar-based video captioning task, we propose a novel Syntax Modulated Caption Generator (SMCG) incorporated in an encoder-decoder-reconstructor architecture. The proposed SMCG takes video semantic representati…
UVMap-ID: A Controllable and Personalized UV Map Generative Model
University of Trento（特伦托大学）；Ocean University of China（中国海洋大学）；ETH Zürich（苏黎世联邦理工学院）；PicsArt AI Research（PicsArt人工智能研究院）；Georgia Institute of Technology（佐治亚理工学院）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）
本文提出 UVMap-ID，通过微调集成人脸融合模块的文本到图像扩散模型，并利用带文本与 Face ID 标注的小规模属性均衡数据集，实现可控、个性化的人物 UV Map 生成与多维质量评估。
Pose-ICL: 3D-Aware In-Context Learning for Pose-Controllable Subject Customization
College of Electronic and Information Engineering, Tongji University, Shanghai, China（同济大学电子与信息工程学院）；State Key Laboratory of Autonomous Intelligent Unmanned Systems, Frontiers Science Center for Intelligent Autonomous Systems, Ministry of Education, Shanghai, China（自主智能无人系统国家重点实验室，智能自主系统前沿科学中心，教育部，上海，中国）
Ctrl-GenAug: Controllable Generative Augmentation for Medical Sequence Classification
National-Regional Key Technology Engineering Laboratory for Medical Ultrasound（国家级区域医疗超声关键技术工程实验室）；School of Biomedical Engineering（生物医学工程学院）；Medical School（医学院）；Shenzhen University（深圳大学）；Medical UltraSound Image Computing (MUSIC) Lab（医学超声图像计算（MUSIC）实验室）；School of Artificial Intelligence（人工智能学院）；The Hong Kong University of Science and Technology（香港科学与技术大学）；Department of Electronic and Computer Engineering（电子与计算机工程系）；The University of Manchester（曼彻斯特大学）；The Third Affiliated Hospital of Sun Yat-sen University（中山大学第三附属医院）；Longgang District People’s Hospital of Shenzhen（深圳龙岗区人民医院）；The Second Affiliated Hospital of The Chinese University of Hong Kong（香港中文大学第二附属医院）；School of Biomedical Engineering and Informatics（生物医学工程与信息学学院）；NIHR Manchester Biomedical Research Centre（NIHR曼彻斯特生物医学研究中心）
