publications

2026

1. NeurIPS

   

   Past as State, Present as Attention: Persistent-State Blockwise Flow Matching for Long-Horizon Co-Speech Motion Generation

   Yi Liu, Xiangyue Zhang, Jianfang Li, and 3 more authors

   2026

   Bib PDF Video Website

   @misc{liu2026paspa,
     title = {Past as State, Present as Attention: Persistent-State Blockwise Flow Matching for Long-Horizon Co-Speech Motion Generation},
     author = {Liu, Yi and Zhang, Xiangyue and Li, Jianfang and Ma, Jia and He, Yisheng and Ren, Jianqiang},
     year = {2026},
   }

2. arXiv

   

   Disentangling Generation and Regression in Stochastic Interpolants for Controllable Image Restoration

   Yi Liu, Jia Ma, Wengen Li, and 3 more authors

   arXiv preprint arXiv:2605.21381, 2026

   arXiv Bib PDF

   @article{liu2026disi,
     title = {Disentangling Generation and Regression in Stochastic Interpolants for Controllable Image Restoration},
     author = {Liu, Yi and Ma, Jia and Li, Wengen and Guan, Jihong and Zhou, Shuigeng and Zhang, Yichao},
     journal = {arXiv preprint arXiv:2605.21381},
     year = {2026},
     url = {https://arxiv.org/abs/2605.21381}
   }

3. arXiv

   

   SADER: Structure-Aware Diffusion Framework with DEterministic Resampling for Multi-Temporal Remote Sensing Cloud Removal

   Yifan Zhang, Qian Chen, Yi Liu, and 2 more authors

   arXiv preprint arXiv:2602.00536, 2026

   Yi Liu is a co-first author.

   arXiv Bib PDF

   @article{zhang2026sader,
     title = {SADER: Structure-Aware Diffusion Framework with DEterministic Resampling for Multi-Temporal Remote Sensing Cloud Removal},
     author = {Zhang, Yifan and Chen, Qian and Liu, Yi and Li, Wengen and Guan, Jihong},
     journal = {arXiv preprint arXiv:2602.00536},
     year = {2026},
     url = {https://arxiv.org/abs/2602.00536},
     note = {Yi Liu is a co-first author.}
   }

2025

1. CVPR

   

   Effective Cloud Removal for Remote Sensing Images by an Improved Mean-Reverting Denoising Model with Elucidated Design Space

   Yi Liu, Wengen Li, Jihong Guan, and 2 more authors

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025

   Abs Bib HTML PDF Code

   Cloud removal (CR) remains a challenging task in remote sensing image processing. Although diffusion models (DM) exhibit strong generative capabilities, their direct applications to CR are suboptimal, as they generate cloudless images from random noise, ignoring inherent information in cloudy inputs. To overcome this drawback, we develop a new CR model EMRDM based on mean-reverting diffusion models (MRDMs) to establish a direct diffusion process between cloudy and cloudless images. Compared to current MRDMs, EMRDM offers a modular framework with updatable modules and an elucidated design space, based on a reformulated forward process and a new ordinary differential equation (ODE)-based backward process. Leveraging our framework, we redesign key MRDM modules to boost CR performance, including restructuring the denoiser via a preconditioning technique, reorganizing the training process, and improving the sampling process by introducing deterministic and stochastic samplers. To achieve multi-temporal CR, we further develop a denoising network for simultaneously denoising sequential images. Experiments on mono-temporal and multi-temporal datasets demonstrate the superior performance of EMRDM. Our code is available at https://github.com/Ly403/EMRDM.

   @inproceedings{liu2025effective,
     title = {Effective Cloud Removal for Remote Sensing Images by an Improved Mean-Reverting Denoising Model with Elucidated Design Space},
     author = {Liu, Yi and Li, Wengen and Guan, Jihong and Zhou, Shuigeng and Zhang, Yichao},
     booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
     year = {2025},
     pages = {17851--17861},
     url = {https://arxiv.org/pdf/2503.23717},
   }