Stefan Baumann

I'm an ELLIS PhD student in the CompVis group, advised by Björn Ommer (LMU) and Peter Kontschieder (Meta). My research focuses on furthering our understanding of generative models (primarily diffusion) and how we can use these insights to excert more control over them.

Prior to my PhD, I've spent time as a ML research intern at Intel Labs (Emergent AI Research, German Ros, Benjamin Ummenhofer) and Sony (AI Speech and Sound Group, Stefan Uhlich, Giorgio Fabbro, Thomas Kemp), and as a ML researcher at a startup (Duc Tam Nguyen). I obtained my Bachelor's in EECS and my Master's in Signal Processing and Machine Learning at KIT.

I'm always open to collaborations and supervising Master's theses (primarily at LMU Munich & TU Munich, but potentially also other institutions). Just drop me an email :)

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Research

I'm interested in generative AI and its applications to computer vision. Representative papers are highlighted.

T2I diffusion models alredy knew how to do fine-grained control, we just had to learn out how to leverage this capability.

Scan order matters for SSMs in vision tasks.

Efficient generative monocular depth estimation via flow matching from noisy RGB to depth.

Incredibly efficient transformer-based image diffusion models that can generate megapixel-resolution images in pixel space.

Making high-resolution T2I diffusion fast and increasing resolutions to multiple megapixels by adding flow matching-based superresolution stages in latent space.

This website is based on Jon Barron's website, whose code is available on GitHub.