李重仪，南开大学教授、博士生导师，国家优秀青年科学基金（海外）、天津市杰出青年基金获得者，天津市视觉计算与智能感知重点实验室副主任，天津市青年科技工作者协会副主任委员，现领导PI Lab: 摄影与成像实验室。主要研究方向为人工智能、计算机视觉与机器学习，聚焦计算成像、图像增强与复原、图像生成与编辑、多模态大语言模型等，已在TPAMI、TIP、NeurIPS、ICML、CVPR、ICCV等国际顶级期刊与会议发表论文150余篇，谷歌学术引用超30000次，其中第一作者单篇最高引用超过3000次，成果包括30篇ESI高被引论文与10篇ESI热点论文、9篇论文入选Oral/Highlight/Spotlight、多篇论文入选TPAMI/TIP Popular Documents、曾获IEEE信号处理学会（SPS）最佳论文奖、Engineering Applications of Artificial Intelligence最佳论文奖、MLMC最佳论文奖、Pattern Recognition最佳论文提名奖、世界人工智能大会青年优秀论文提名奖、《信号处理》优秀论文奖，授权国内外发明专利17项。研究成果已在华为国产AI平台、联发科天玑9300旗舰芯片、全球最大潜水社区PADI在线水下色彩还原系统、OPPO手机、腾讯生成大模型、商汤生成大模型、Stable Diffusion WebUI、新加坡Nanoveu、香港Perfection Aligner System等20余家国内外公司落地应用，并3次获得省部级科技论文一等奖、获得中国图象图形学学会自然科学奖二等奖、中国汽车工程协会科技进步二等奖、3次获得中国人工智能学会昇思MindSpore学术基金（其中两次获评优秀项目）、获得华为产学研优秀合作项目奖、科普作品入选中国科协“科普中国”2026年中国优质科普资源。研究成果被中国日报、中国青年报、科技日报、中国新闻网、中国科技网、中国高新网、中国科学技术协会、天津市人民政府新闻办公室、中宏网、科学网、今晚报、津云新闻等媒体报道。他连续6年(2021-2026)入选斯坦福全球前2%顶尖科学家榜（包括年度及终身影响力榜单），连续两年（2024-2025）入选科睿唯安全球高被引科学家、连续3年（2023-2025）入选爱思唯尔中国高被引学者、入选2025年ScholarGPS全球前0.05%顶尖学者榜单，入选2026年度Research.com全球顶尖科学家排名。曾受中共中央委员会主办的《光明日报》邀请，撰写人工智能专题文章。作为技术嘉宾，参与央视总台“科晚”栏目，围绕“AI能否代写论文”等热点问题，分享技术视角下的观察与思考。2026年受新华社邀请，参与“论文AI生成率检测存在的问题和学术、社会影响”重大主题采访，作为计算机领域专家提供技术分析与政策建议，相关观点被纳入调研报道，为有关部门建言献策提供重要参考。

近期代表性论文：

• C. Li, C. Guo, R. Feng, S. Zhou, and C. C. Loy, CuDi: Curve Distillation for Efficient and Controllable Exposure Adjustment, In TPAMI, 2026.

• C. Qin, Z. Zhang, Z. Chang, C. Guo, R. Wang, T. Hu, M. Cheng, and C. Li, Dotting the Eye: An Intent-Driven Image Retouching Agent for Visual Focus Enhancement, In ECCV, 2026.

• Y. Meng, C. Wu, X. Liu, C. Guo, Z. Liang, L. Lei, J. Liang, H. Zeng, C. Li, and L. Zhang, FlowPainter: Inpainting Optical Flow via Confidence-Guided Completion, In ECCV, 2026.

• R. Wu, X. He, M. Cheng, T. Yang, Y. Zhang, Z. Kang, X. Cai, X. Wei, C. Guo, C. Li, and M. Cheng, Infinite-World: Scaling Interactive World Models to 1000-Frame Horizons via Pose-Free Hierarchical Memory, In ICML, 2026.

• S. Han, W. Liao, H. Fan, H. Dong, R. Zhang, C. Guo, and C. Li, DNF-SR: Dual-Input and Negative-Aware Feature Fine-Tuning for Real-World Image Super-Resolution, In CVPR, 2026.

• B. Xue, Z. Duan, Q. Yan, W. Wang, H. Liu, C. Guo, C. Li, C. Li, and J. Lyu, Stand-In: A Lightweight and Plug-and-Play Identity Control for Video Generation, In CVPR, 2026.

• T. Zhang, Z. Duan, C. Guo, P. Jiang, B. Li, M. Cheng, and C. Li, Time-Aware One Step Diffusion Network for Real-World Image Super-Resolution, In CVPR, 2026.

• C. Wu, L. Lei, F. Li, C. Guo, D. Kong, X. Qin, Z. Wang, M. Cheng, and C. Li, YOSE: You Only Select Essential Tokens for Efficient DiT-based Video Object Removal, In CVPR, 2026.

• C. Wu, J. Fu, C. Guo, S. Han, and C. Li⁺, VTinker: Guided Flow Upsampling and Texture Mapping for High-Resolution Video Frame Interpolation, In AAAI, 2026.

• Z. Chang, Z. Duan, J. Zhang, C. Guo, S. Liu, H. Chun, H. Park, Z. Liu, and C. Li, PerTouch: VLM-Driven Agent for Personalized and Semantic Image Retouching, In AAAI, 2026.

• S. Han, H. Fan, J. Fu, L. Li, T. Li, J. Cui, Y. Wang, Y. Tai, J. Sun, C. Guo, and C. Li, EvalMuse-40K: A Reliable and Fine-Grained Benchmark with Comprehensive Human Annotations for Text-to-Image Generation Model Evaluation, In AAAI, 2026.

• S. Han, C. Wu, C. Guo, Z. Duan, Z. Li, M. Cheng, and C. Li. Accurate Motion Estimation with Bézier Control Point for Efficient Frame Interpolation. In TIP, 2026.

• J. Yuan, Y. Li, Y. Zhang, C. Guo, X. Tang, R. Wang, and C. Li. 3D-UIR: 3D Gaussian for Underwater 3D Scene Reconstruction via Physics-Based Appearance-Medium Decoupling. In TIP, 2026.

• C. Wang, H. Li, C. Li, H. Liu, X. Tang, and S. Kwong. Expose Camouflage in the Water: Underwater Camouflaged Instance Segmentation and Dataset. In TIP, 2026.

• W. Wen, T. Zhang, Q. Zhao, Z. Zheng, C. Guo, X. Shao, and C. Li, Incorporating Uncertainty-Guided and Top-k Codebook Matching for Real-World Blind Image Super-Resolution. In TIP, 2026.

• Y. Zhang, X. Jiang, C. L. Guo, X. Tang, G. Wang, W. Li, X. Liu, and C. Li, Boosting Guided Depth Super-Resolution Through Large Depth Estimation Model and Alignment-then-Fusion Strategy. In CVMJ (IF: 18.3), 2026.

• Y. Meng, X. Jin, L. Lei, C. Guo, C. Li, UltraLED: Learning to See Everything in Ultra-High Dynamic Range Scenes, In NeurIPS, 2025.

• R. Wu, X. wang, L. Liu, C. Guo, J. Qiu, C. Li, L. Huang, Z. Su, and M. Cheng, DIPO: Dual-State Images Controlled Articulated Object Generation Powered by Diverse Data, In NeurIPS, 2025.

• Z. Liang, Z. Li, S. Zhou, C. Li, and C. C. Loy, Control Color: Multimodal Diffusion-Based Interactive Image Colorization, IJCV, 2025.

• J. Fu, C. Guo, and C. Li, DetectAnyLLM: Towards Generalizable and Robust Detection of Machine-Generated Text Across Domains and Models, In ACMMM (Oral), 2025.

• Z. Duan, J. Zhang, X. Jin, Z. Zhang, Z. Xiong, D. Zou, J. Ren, C. Guo, and C. Li, DiT4SR: Taming Diffusion Transformer for Real-World Image Super-Resolution, In ICCV, 2025.

• F. Ou, C. Li, S. Wang, S. Kwong, MR-FIQA: Face Image Quality Assessment with Multi-Reference Representations from Synthetic Data Generation, In ICCV, 2025.

• J. He, C. Li, S. Wang, S. Kwong, Joint Semantic and Rendering Enhancements in 3D Gaussian Modeling with Anisotropic Local Encoding, In ICCV, 2025.

• J. Fu, S. Liu, Z. Liu, C. Guo, H. Park, R. Wu, G. Wang, and C. Li, Iterative Predictor-Critic Code Decoding for Real-World Image Dehazing. In CVPR, 2025.

• X. Jin, S. Niklaus, Z. Zhang, Z. Xia, C. Guo, Y. Yang, J. Chen, and C. Li, Classic Video Denoising in a Machine Learning World: Robust, Fast, and Controllable. In CVPR, 2025.

• Z. Duan, J. Zhang, Z. Lin, X. Wang, D. Zhou, C. Guo, and C. Li, DiffRetouch: Using Diffusion to Retouch on the Shoulder of Experts. In AAAI, 2025.

• Z. Duan, J. Zhang, S. Liu, Z. Lin, C. Guo, D. Zhou, J. Ren, and C. Li, A Diffusion-Based Framework for Occluded Object Movement. In AAAI, 2025.

• S. Liu, Z. Duan, J. Ouyang, J. Fu, H. Park, Z. Liu, C. Guo, and C. Li, FaceMe: Robust Blind Face Restoration With Personal Identification. In AAAI, 2025.

• L. Chen, Y. Huang, J. Dong, Q. Xu, S. Kwong, H. Lu, H. Lu, and C. Li, Deep Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future. In CSUR (IF: 28), 2025.

• J. He, C. Li, S. Wang, and S. Kwong, Improving Point Cloud Robustness Through Perturbation Simulation and Distortion-Guided Feature Augmentation. In TIP, 2025.

• M. Zha, G. Wang, Y. Pei, T. Li, X. Tang, C. Li, Y. Yang, and H. Shen, Heterogeneous Experts and Hierarchical Perception for Underwater Salient Object Detection. In TIP, 2025.

• Y. Dai, C. Li, S. Zhou, R. Feng, and C. C. Loy, Flare7K++: Mixing Synthetic and Real Datasets for Nighttime Flare Removal and Beyond. In TPAMI, 2024.

• Y. Wu, G. Wang, S. Liu, Y. Yang, W. Li, X. Tang, S. Gu, C. Li, and H. Shen, Towards a Flexible Semantic Guided Model for Single Image Enhancement and Restoration. In TPAMI, 2024.

• X. Jin, P. Jiao, Z. Duan, X. Yang, C. Li, C. Guo, and B. Ren, Lighting Every Darkness with 3DGS: Fast Training and Real-Time Rendering for HDR View Synthesis. In NeurIPS, 2024.

• F. Ou, C. Li, S. Wang, and S. Kwong, CLIB-FIQA: Face Image Quality Assessment with Confidence Calibration. In CVPR, 2024.

• R. Wu, L. Chen, T. Yang, C. Guo, C. Li, and X. Zhang, LAMP: Learn A Motion Pattern for Few-Shot Video Generation. In CVPR, 2024.

• Y. Dai, S. Zhou, Q. Li, C. Li, and C. C. Loy, Learning Inclusion Matching for Animation Paint Bucket Colorization. In CVPR, 2024.

• X. Lv, S. Zhang, C. Wang, Y. Zheng, B. Zhong, C. Li, and L. Nie, Fourier Priors-Guided Diffusion for Zero-Shot Joint Low-Light Enhancement and Deblurring. In CVPR, 2024.

• R. Feng, C. Li, and C. C. Loy, Kalman-Inspired Feature Propagation for Video Face Super-Resolution. In ECCV, 2024.

• C. Qin, R. Wu, Z. Liu, X. Lin, C. Guo, H. H. Park, and C. Li, Restore Anything with Masks: Leveraging Mask Image Modeling for Blind All-in-One Image Restoration. In ECCV, 2024.

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