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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

Jan 27, 2026 Four papers accepted by ICLR’26.
Dec 30, 2025 I’m happy to share our new work UniPercept, which tackles a key blind spot of today’s multimodal LLMs: perceptual-level image understanding — how images look and feel to humans — covering aesthetics, quality, structure, and texture. Our release includes UniPercept-Bench, a unified benchmark spanning IAA/IQA/ISTA and supporting both Visual Rating (VR) and Visual Question Answering (VQA) evaluations. We also introduce the UniPercept baseline model to generalize across VR and VQA settings. Beyond benchmarking, UniPercept can be used as a reward model for post-training text-to-image systems and as a perceptual diagnostic tool for analyzing model outputs and datasets. [Homepage] [GitHub] [ UniPercept-Bench] [ UniPercept Model] [Paper].
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 Lumina-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]

selected publications

  1. arXiv

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    UniPercept: Towards Unified Perceptual-Level Image Understanding across Aesthetics, Quality, Structure, and Texture

    Shuo Cao, Jiayang Li, Xiaohui Li, Yuandong Pu, Kaiwen Zhu, Yuanting Gao, Siqi Luo, Yi Xin, Qi Qin,  ..., and Yihao Liu†

    arXiv preprint arXiv:2512.21675, 2025

  2. ICLR

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    Omni-Weather: Unified Multimodal Foundation Model for Weather Generation and Understanding

    arXiv preprint arXiv:2512.21643, 2025

  3. ICLR

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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

  4. 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

  5. ICLR

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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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

  21. 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

  22. 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

  23. 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

  24. 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

  25. 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

  26. 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

  27. 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

  28. 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

  29. 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

  30. 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

  31. 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