Yihao LIU

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