Yihao LIU

photo_lyh.jpg
Google scholar Github XiaoHongShu

I am a Research Scientist at the Shanghai Artificial Intelligence Laboratory, where I lead a team focusing on multimodal generation and understanding. I earned my Bachelor’s degree in 2018 and my Ph.D. in 2023, both from the University of Chinese Academy of Sciences (UCAS). During my doctoral studies, I was affiliated with the Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences, under the supervision of Prof. Yu Qiao and Prof. Chao Dong. My research lies at the intersection of computer vision, generative modeling, and scientific intelligence, with particular emphasis on multimodal foundation models and image/video enhancement.

Throughout my student journey, I have been honored with prestigious awards, including the President’s Award of the Chinese Academy of Sciences, the Zhu Li Yue Hua Outstanding Doctoral Student Award, the CAS Excellent Youth League Member Award, the Beijing Outstanding Graduate Award, the SIAT President’s Innovation Award, as well as the CVMJ 2025 Best Paper Honorable Mention Award.

I have also excelled in multiple international and national competitions, such as 1st place in the PIRM 2018 Perceptual Image Super-Resolution Challenge, 1st place in the AIM 2020 Video Frame Interpolation Challenge, 2nd place in the NTIRE 2021 HDR Enhancement Challenge, 3rd place in the UDC 2020 Under-Display Camera Restoration Challenge. I serve as a reviewer for various top journals and conferences, including TPAMI, TIP, TCSVT, TMM, CVPR, ICCV, ECCV, NeurIPS, etc.

Current Research Focus

My current research focuses on pioneering a new generation of multimodal foundation models that integrate generation and understanding within a unified architecture. Specifically:

  • Unified Multimodal Architectures: Designing new-generation frameworks (e.g., discrete diffusion, autoregressive hybrids) that integrate text, image, video, and audio tasks, enabling coherent cross-modal representation, reasoning, and generation.
  • Knowledge-Driven and Causality-Aware Modeling: Embedding structured world knowledge, physical realism, and causal reasoning into multimodal models, moving beyond perceptual fidelity toward scientifically grounded and logically consistent outputs.
  • General Low-Level Vision Models: Consolidating diverse low-level vision tasks — restoration, enhancement, style transfer, and dense prediction — into a robust multimodal framework, advancing detail recovery, fidelity, and generalization for real-world applications.
  • Post-training and Reward Alignment: Developing multimodal alignment and reinforcement learning paradigms, incorporating human preference modeling and expert feedback, to ensure outputs that are not only high-quality and aesthetic but also reliable, interpretable, and scientifically valid.

I am open to collaboration and discussions. Feel free to reach out at liuyihao@pjlab.org.cn or liuyihao14@mails.ucas.ac.cn

news

Oct 21, 2025 We present PICABench, a new benchmark and evaluation protocol for assessing physical realism in image editing — an often overlooked dimension in current generative models. PICABench systematically evaluates the physical consequences across eight sub-dimensions spanning optics, mechanics, and state transitions, with a reliable PICAEval protocol combining VLM-as-a-judge and region-level human annotations. We also build PICA-100K, a dataset for learning physics from videos. Evaluations show that physical realism remains a major challenge. PICABench aims to drive the next wave of physics-aware, causally consistent image editing. [Homepage] [GitHub] [ PICABench Dataset] [ PICA-100K Dataset] [Paper].
Sep 10, 2025 We are excited to announce Lumina-DiMOO, our latest unified multimodal generation and understanding model built upon an advanced discrete diffusion architecture. This framework demonstrates the strong potential of multimodal diffusion large language models (dLLM) to unify diverse tasks within a single, streamlined architecture, while delivering state-of-the-art performance that surpasses many existing unified models. Learn more and explore resources: [Homepage] [GitHub] [HuggingFace].
Sep 01, 2025 We introduce ArtiMuse, a multimodal large language model (MLLM) for professional aesthetic understanding, which is trained on ArtiMuse-10K, a meticulously curated, expert-annotated dataset. ArtiMuse-10K systematically defines eight explainable and fine-grained aesthetic attributes (e.g., Composition & Design, Visual Elements & Structure), with a wide coverage of diverse visual domains, including graphic design, 3D design, AIGC-generated images, photography, and painting & calligraphy. [Paper] [Homepage] [GitHub] [Online Demo v1.0] Note: ArtiMuse was officially released at WAIC 2025, in the forum “Evolving with AI: The Iteration and Resilience of Artistic Creativity”.
Jun 26, 2025 Our video restoration method DiffVSR was accepted by ICCV2025. [Paper] [Homepage]
Apr 22, 2025 Our video colorization method TCVC has won the CVMJ 2025 Best Paper Honorable Mention Award.
Apr 01, 2025 We present Lunima-OmniLV (abbreviated as OmniLV), a universal multimodal multi-task framework for low-level vision that addresses over 100 sub-tasks across four major categories, including image restoration, image enhancement, weak-semantic dense prediction, and stylization. [Paper] [Homepage]
Jul 18, 2024 GenLV was accepted by ACM MM2024. GenLV is a successive work of PromptGIP, which further broadens the tasks and improves performance. The paper can be found at here.
Jul 01, 2024 Two papers were accepted by ECCV2024. By analyzing the relationships between image degradations, GRIDS propose a grouped learning method to deal with multiple-degradation restoration. X-Restormer is a new general image restoration backbone network, which possesses good task generality and achieves competitive performance across a variety of restoration tasks.

selected publications

  1. arXiv
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    PICABench: How Far Are We from Physically Realistic Image Editing?
    Yuandong Pu, Le Zhuo, Songhao Han, Jinbo Xing, Kaiwen Zhu, Shuo Cao, Bin Fu, Si Liu, Hongsheng Li, Yu Qiao, Wenlong Zhang, Xi Chen, and Yihao Liu†
    arXiv preprint arXiv:2510.17681, 2025
  2. arXiv
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    FlashVSR: Towards Real-Time Diffusion-Based Streaming Video Super-Resolution
    Junhao Zhuang, Shi Guo, Xin Cai, Xiaohui Li, Yihao Liu, Chun Yuan, and Tianfan Xue
    arXiv preprint arXiv:2510.12747, 2025
  3. arXiv
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    LinearSR: Unlocking Linear Attention for Stable and Efficient Image Super-Resolution
    Xiaohui Li, Shaobin Zhuang, Shuo Cao, Yang Yang, Yuandong Pu, Qi Qin, Siqi Luo, Bin Fu, and Yihao Liu†
    arXiv preprint arXiv:2510.08771, 2025
  4. arXiv
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    Lumina-dimoo: An omni diffusion large language model for multi-modal generation and understanding
    Yi Xin, Qi Qin, Siqi Luo, Kaiwen Zhu, Juncheng Yan, Yan Tai, Jiayi Lei, Yuewen Cao, Keqi Wang, Yibin Wang,  ..., and Yihao Liu†
    arXiv preprint arXiv:2510.06308, 2025
  5. arXiv
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    Artimuse: Fine-grained image aesthetics assessment with joint scoring and expert-level understanding
    Shuo Cao, Nan Ma, Jiayang Li, Xiaohui Li, Lihao Shao, Kaiwen Zhu, Yu Zhou, Yuandong Pu, Jiarui Wu, Jiaquan Wang, Bo Qu, Wenhai Wang, Yu Qiao, Dajuin Yao, and Yihao Liu†
    arXiv preprint arXiv:2507.14533, 2025
  6. arXiv
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    OneVAE: Joint Discrete and Continuous Optimization Helps Discrete Video VAE Train Better
    Yupeng Zhou, Zhen Li, Ziheng Ouyang, Yuming Chen, Ruoyi Du, Daquan Zhou, Bin Fu, Yihao Liu, Peng Gao, Ming-Ming Cheng, and  others
    arXiv preprint arXiv:2508.09857, 2025
  7. arXiv
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    Lumina-omnilv: A unified multimodal framework for general low-level vision
    Yuandong Pu, Le Zhuo, Kaiwen Zhu, Liangbin Xie, Wenlong Zhang, Xiangyu Chen, Peng Gao, Yu Qiao, Chao Dong, and Yihao Liu†
    arXiv preprint arXiv:2504.04903, 2025
  8. ICCV
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    DiffVSR: Revealing an Effective Recipe for Taming Robust Video Super-Resolution Against Complex Degradations
    Xiaohui Li*, Yihao Liu*†, Shuo Cao, Ziyan Chen, Shaobin Zhuang, Xiangyu Chen, Yinan He, Yi Wang, and Yu Qiao
    arXiv preprint arXiv:2501.10110, 2025
  9. TIP
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    Learning to see low-light images via feature domain adaptation
    Qirui Yang, Qihua Cheng, Huanjing Yue, Le Zhang, Yihao Liu, and Jingyu Yang
    IEEE Transactions on Image Processing, 2025
  10. ECCV
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    GRIDS: Grouped Multiple-Degradation Restoration with Image Degradation Similarity
    Shuo Cao*, Yihao Liu*, Wenlong Zhang, Yu Qiao, and Chao Dong
    In European Conference on Computer Vision, 2024
  11. ECCV
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    A Comparative Study of Image Restoration Networks for General Backbone Network Design
    Xiangyu Chen, Zheyuan Li, Yuandong Pu, Yihao Liu, Jiantao Zhou, Yu Qiao, and Chao Dong
    In European Conference on Computer Vision, 2024
  12. ICML
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    Unifying Image Processing as Visual Prompting Question Answering
    Yihao Liu*, Xiangyu Chen*, Xianzheng Ma*, Xintao Wang, Jiantao Zhou, Yu Qiao, and Chao Dong
    In Proceedings of the 41st International Conference on Machine Learning (ICML), 2024
  13. ACM MM
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    Learning A Low-Level Vision Generalist via Visual Task Prompt
    Xiangyu Chen, Yihao Liu, Yuandong Pu, Wenlong Zhang, Jiantao Zhou, Yu Qiao, and Chao Dong
    In Proceedings of the 32nd ACM International Conference on Multimedia, 2024
  14. CVMJ
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    Temporally Consistent Video Colorization with Deep Feature Propagation and Self-Regularization Learning
    Yihao Liu*, Hengyuan Zhao*, Kelvin CK Chan, Xintao Wang, Chen Change Loy, Yu Qiao, and Chao Dong
    Computational Visual Media, 2024
  15. CVPR
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    DegAE: A New Pretraining Paradigm for Low-Level Vision
    Yihao Liu, Jingwen He, Jinjin Gu, Xiangtao Kong, Yu Qiao, and Chao Dong
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
  16. TPAMI
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    Evaluating the Generalization Ability of Super-Resolution Networks
    Yihao Liu, Hengyuan Zhao, Jinjin Gu, Yu Qiao, and Chao Dong
    IEEE Transactions on pattern analysis and machine intelligence, 2023
  17. CVPR
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    Masked Image Training for Generalizable Deep Image Denoising
    Haoyu Chen, Jinjin Gu, Yihao Liu, Salma Abdel Magid, Chao Dong, Qiong Wang, Hanspeter Pfister, and Lei Zhu
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
  18. TPAMI
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    CP3: Unifying Point Cloud Completion by Pretrain-Prompt-Predict Paradigm
    Mingye Xu, Yali Wang, Yihao Liu, Tong He, and Yu Qiao
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
  19. TMM
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    Very Lightweight Photo Retouching Network with Conditional Sequential Modulation
    Yihao Liu*, Jingwen He*, Xiangyu Chen, Zhengwen Zhang, Hengyuan Zhao, Chao Dong, and Yu Qiao
    IEEE Transactions on Multimedia, 2022
  20. TPAMI
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    Blind Image Super-Resolution: A Survey and Beyond
    Anran Liu, Yihao Liu, Jinjin Gu, Yu Qiao, and Chao Dong
    IEEE transactions on pattern analysis and machine intelligence, 2022
  21. TPAMI
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    RankSRGAN: Super Resolution Generative Adversarial Networks with Learning to Rank
    Wenlong Zhang, Yihao Liu, Chao Dong, and Yu Qiao
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
  22. TPAMI
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    Interactive Multi-Dimension Modulation for Image Restoration
    Jingwen He, Chao Dong, Yihao Liu, and Yu Qiao
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
  23. ICCV
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    Learn to Match: Automatic Matching Network Design for Visual Tracking
    Zhipeng Zhang, Yihao Liu, Xiao Wang, Bing Li, and Weiming Hu
    In International Conference on Computer Vision (ICCV), 2021
  24. arXiv
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    Discovering" Semantics" in Super-Resolution Networks
    Yihao Liu*, Anran Liu*, Jinjin Gu, Zhipeng Zhang, Wenhao Wu, Yu Qiao, and Chao Dong
    arXiv preprint arXiv:2108.00406, 2021
  25. AAAI
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    FD-GAN: Generative Adversarial Networks with Fusion-Discriminator for Single Image Dehazing
    Yu Dong*, Yihao Liu*, He Zhang, Shifeng Chen, and Yu Qiao
    In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2020
  26. ECCV
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    Conditional Sequential Modulation for Efficient Global Image Retouching
    Jingwen He*, Yihao Liu*, Yu Qiao, and Chao Dong
    In European Conference on Computer Vision (ECCV), 2020
  27. ECCVW
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    Enhanced Quadratic Video Interpolation
    Yihao Liu*, Liangbin Xie*, Li Siyao, Wenxiu Sun, Yu Qiao, and Chao Dong
    In European Conference on Computer Vision (ECCV) Workshops, 2020
  28. ICCV
    RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution
    Wenlong Zhang, Yihao Liu, Chao Dong, and Yu Qiao
    In International Conference on Computer Vision (ICCV), 2019
  29. ECCVW
    ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
    Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, and Chen Change Loy
    In Proceedings of the European conference on computer vision (ECCV) workshops, 2018