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jingyunliang/README.md

Jingyun Liang visitorsGitHub Followers

Email / Homepage / Google Scholar / Github

I am currently a Senior Research Scientist at Adobe Research, Seattle. Before this, I worked at Alibaba as an Algorithm Expert, after graduation from the Computer Vision Lab, ETH Zürich. During my PhD, I was under the supervision of Prof. Luc Van Gool and Prof. Radu Timofte. My research is focused on visual generation and low-level vision, such as image and video generation/ editing / restoration.

I am looking for Research Scientist Interns on image and video generation/ editing/ restoration 
for Adobe Research, as well as academic collbrators. 
Please send your CV to me if you are interested 😇

🚀 News

  • 2026-09: I joined Adobe Research as a Senior Research Scientist.
  • 2026-06: See our paper on exploring the 3D awareness of video diffusion models (MeshToken).
  • 2026-04: Our work on decomposed motion editing (RealisMotion) has been accepted by ICML2026.
  • 2026-02: Our work on autoregressive video generation (Lumos-1) has been accepted by ICLR2026.

🌱 Repositories

Topic Title Badge
real-world video denoising Practical Real Video Denoising with Realistic Degradation Model arXivGitHub Stars
event-based image deblurring Event-based Fusion for Motion Deblurring with Cross-modal Attention, ECCV2022 arXivGitHub Stars
reference image SR Reference-based Image Super-Resolution with Deformable Attention Transformer, ECCV2022 arXivGitHub Stars
interpretable video restoration Towards Interpretable Video Super-Resolution via Alternating Optimization, ECCV2022 arXivGitHub Stars
transformer-based video restoration Recurrent Video Restoration Transformer with Guided Deformable Attention arXivGitHub Starsdownload google colab logo
transformer-based video restoration VRT: A Video Restoration Transformer arXivGitHub Starsdownload google colab logo
transformer-based image restoration SwinIR: Image Restoration Using Swin Transformer arXivGitHub Starsdownload google colab logo
real-world image denoising Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis arXivGitHub Stars
real-world image SR Designing a Practical Degradation Model for Deep Blind Image Super-Resolution, ICCV2021 arXivGitHub Stars
blind image SR Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution, ICCV2021 arXivGitHub Starsdownload google colab logo
blind image SR Flow-based Kernel Prior with Application to Blind Super-Resolution, CVPR2021 arXivGitHub Stars
normalizing flow-based image SR and image rescaling Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling, ICCV2021 arXivGitHub Starsdownload google colab logo
image/ video restoration Image/ Video Restoration Toolbox GitHub StarsdownloadGitHub Forks

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  1. RVRT RVRT Public

    Recurrent Video Restoration Transformer with Guided Deformable Attention (NeurlPS2022, official repository)

    Python 453 39

  2. VRT VRT Public

    VRT: A Video Restoration Transformer (official repository)

    Python 1.5k 140

  3. SwinIR SwinIR Public

    SwinIR: Image Restoration Using Swin Transformer (official repository)

    Python 5.6k 665

  4. HCFlow HCFlow Public

    Official PyTorch code for Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling (HCFlow, ICCV2021)

    Python 194 29

  5. MANet MANet Public

    Official PyTorch code for Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution (MANet, ICCV2021)

    Python 177 25

  6. FKP FKP Public

    Official PyTorch code for Flow-based Kernel Prior with Application to Blind Super-Resolution (FKP, CVPR2021)

    Python 150 20