December 2023: The result of my internship Cache Me if You Can is out on ArXiv.

December 2023: I completed my internship at Meta.

October 2023: Our work S4C on self-supervised semantic scene completion was accepted to 3DV 2024.

August 2023: I started a 14 week internship at Meta GenAI with Jialiang Wang in Menlo Park.

June 2023: Check out our video presentation and demo video of Behind the Scenes.

February 2023: Behind the Scenes: Density Fields for Single View Reconstruction was accepted at CVPR 2023.

January 2023: Pre-Print of new paper Behind the Scenes: Density Fields for Single View Reconstruction is online.

April 2022: Started Ph.D. in Computer Vision at the chair of Daniel Cremers.

March 2022: De-render3D was accepted at CVPR 2022.

September 2021: Finished M.Sc. Computer Science at University of Oxford.

March 2021: MonoRec was accepted at CVPR 2021. Check out our video and open-source code

October 2020: Started M.Sc. Computer Science at University of Oxford.

August 2020: Finished ADL4CV. Check out our video presentation here.

March 2020: Finished my B.Sc. Informatik at TUM.

Always happy to discuss new research ideas.

My aim is to develop methods to understand the 3D world from images and videos. Specifically, I work on methods that can be trained with weak or even no supervision. Through this, I hope to enable more wide-spread applications of such methods, and to improve accuracy by training on large, unlabeled datasets.

At the moment, I focus on the subtasks of 3D reconstruction (both static and dynamic), 3D object discovery, as well as the combination of both. There are several techniques that are relevant in this context. By leveraging multi-view consistency, we can get supervision signals for 3D reconstruction. Through motion segmentation and clustering, especially in 3D rather than 2D, we can detect different objects. Self-supervised feature learning further allows to introduce semantic understanding to the model.

In the past, I have worked on depth prediction from multiple frames while considering moving objects (MonoRec, CVPR 2021) and weakly supervised de-rendering for in-the-wild objects (De-Rendering 3D Objects in the Wild, CVPR 2022). My most recent project deals with self-supervised single view reconstruction and volume rendering.

If you have any questions, feel free to reach out to me!

You can also setup a meeting me with using the widget below! When you book a timeslot, the tool with automatically set up a calendar event and invite you to it.

I am open to supervising ambitious and talented Master and Bachelor students for their thesis. If you want to work with me, please send me an email describing the area / project you would like to work on. Please also attach your CV and up-to-date transcript.

A good approach to finding an area is always to check out the papers I have worked on in the past and extending from there.

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Conference and Workshop Papers
2024
[]		ControlRoom3D: Room Generation using Semantic Proxy Rooms (J Schult, S Tsai, L Höllein, B Wu, J Wang, CY Ma, K Li, X Wang, F Wimbauer, Z He, P Zhang, B Leibe, P Vajda and J Hou), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024.  [bibtex]
[]		Boosting Self-Supervision for Single-View Scene Completion via Knowledge Distillation (K Han, D Muhle, F Wimbauer and D Cremers), In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. ([project page]) [bibtex] [arXiv:2404.07933]
[]		Cache Me if You Can: Accelerating Diffusion Models through Block Caching (F Wimbauer, B Wu, E Schoenfeld, X Dai, J Hou, Z He, A Sanakoyeu, P Zhang, S Tsai, J Kohler, C Rupprecht, D Cremers, P Vajda and J Wang), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. ([project page]) [bibtex] [arXiv:2312.03209]
[]		S4C: Self-Supervised Semantic Scene Completion with Neural Fields (A Hayler, F Wimbauer, D Muhle, C Rupprecht and D Cremers), In 2024 International Conference on 3D Vision (3DV), 2024. ([project page]) [bibtex] [arXiv:2310.07522] [pdf]
2023
[]		Behind the Scenes: Density Fields for Single View Reconstruction (F Wimbauer, N Yang, C Rupprecht and D Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023. ([project page]) [bibtex] [arXiv:2301.07668]
2022
[]		De-rendering 3D Objects in the Wild (F Wimbauer, S Wu and C Rupprecht), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.  [bibtex] [arXiv:2201.02279]
2021
[]		MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera (F. Wimbauer, N. Yang, L. von Stumberg, N. Zeller and D Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. ([project page]) [bibtex] [arXiv:2011.11814]