Ming Gui

PhD Student at Ommer Lab, LMU Munich

ming_gui.jpg

Room 118

Akademiestr. 7, 80799

Munich, Germany

Hi, I’m Ming! I’m a PhD student at the Ommer Lab at LMU Munich, working on generative models, image and video synthesis, and visual understanding.

I completed both my bachelor’s and master’s degrees in Electrical and Computer Engineering at the Technical University of Munich (TUM), where I built the foundation for my current research.

Much of my PhD research focuses on diffusion and flow matching models. Feel free to explore my selected publications below, or visit my Google Scholar profile for the full list.

selected publications

  1. Adapting Self-Supervised Representations as a Latent Space for Efficient Generation
    Adapting Self-Supervised Representations as a Latent Space for Efficient Generation
    Ming Gui*, Johannes Schusterbauer*, Timy Phan, and 4 more authors
    In The Fourteenth International Conference on Learning Representations (ICLR), Apr 2026
    arXiv Code
  2. Denoising, Fast and Slow: Difficulty-Aware Adaptive Sampling for Image Generation
    Denoising, Fast and Slow: Difficulty-Aware Adaptive Sampling for Image Generation
    Johannes Schusterbauer*, Ming Gui*, Yusong Li, and 3 more authors
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2026
    arXiv Project page
  3. TREAD: Token Routing for Efficient Architecture-agnostic Diffusion Training
    TREAD: Token Routing for Efficient Architecture-agnostic Diffusion Training
    Felix Krause, Timy Phan, Ming Gui, and 3 more authors
    In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Oct 2025
    arXiv Project page
  4. DepthFM: Fast Generative Monocular Depth Estimation with Flow Matching
    DepthFM: Fast Generative Monocular Depth Estimation with Flow Matching
    Ming Gui*, Johannes Schusterbauer*, Ulrich Prestel, and 6 more authors
    In Proceedings of the AAAI Conference on Artificial Intelligence, 2025
    Oral arXiv Project page
  5. FMBoost: Boosting Latent Diffusion with Flow Matching
    FMBoost: Boosting Latent Diffusion with Flow Matching
    Johannes Schusterbauer*, Ming Gui*, Pingchuan Ma*, and 4 more authors
    In European Conference on Computer Vision (ECCV), 2024
    Oral arXiv Project page