Denys Rozumnyi

I'm a PhD student at the Computer Vision and Geometry Group, Department of Computer Science, ETH Zürich under supervision of Prof. Marc Pollefeys. I also work closely with Prof. Martin Oswald, Prof. Vittorio Ferrari, and Prof. Jiri Matas.

Previously, I was a research intern at Google Research with Prof. Vittorio Ferrari and at Meta Reality Labs. Before that, I finished my MSc and BSc degrees from CTU in Prague, Center for Machine Perception under supervision of Prof. Jiri Matas.

My research interests are in 3D reconstruction of objects and scenes, object detection, tracking, 6D pose estimation, and deblurring. In particular, I focus on test-time optimization methods to solve those problems.

I'm close to graduating from my PhD and actively looking for positions.

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News

• Mar 2024: Finished my research internship at Meta.
• Sep 2023: My internship work at Google is accepted to NeurIPS 2023.
• July 2023: Two papers accepted at ICCV 2023.

Research

A diffusion-based model that deblurs and recovers shape of fast moving objects from a single image without a known background (for the first time).

We propose a novel method to produce generic 3D room layouts just from 2D segmentation masks, with which we annotate and publicly release 2246 3D room layouts on the RealEstate10k dataset.

We estimate 3D human poses from substantially blurred images, e.g. extension of Shape from Blur to SMPL human body model.

We propose to guide and improve 2D tracking with an explicit object representation, namely the textured 3D shape and 6DoF pose in each video frame.

A new algorithm for finding an unknown number of geometric models.

Extension of Shape from Blur to multiple frames with more complex trajectories and exposure time modeling.

The first method to estimate textured 3D shape and sub-frame 6D motion of fast moving objects from a single frame.

The first deep-learning based approach for fast moving object detection.

We propose DeFMO that given a single image with its estimated background outputs the object's appearance and position in a series of sub-frames as if captured by a high-speed camera (i.e. temporal super-resolution). This is the first deep-learning based approach for FMO deblurring.

Summarization and extension of our GCPR'19 (TbD-NC) and ICCVW'19 (TbD) papers.

We extend TbD pipeline to track fast moving objects in full 6 DoF, estimating simultaneously their 3D motion trajectory, 3D pose and object appearance changes with a time step that is a fraction of the video frame exposure time.

We apply post-processing with dynamic programming and curve fitting to obtain more accurate object trajectories.

We propose a novel approach called Tracking by Deblatting to track fast moving objects.

Our method learns sensor or algorithm properties jointly with semantic depth fusion and scene completion and can also be used as an expert system, eg to unify the strengths of various photometric stereo algorithms.

Introducing fast moving objects for the first time as objects that move over distances larger than their size in one video frame: new problem, new dataset, new metrics, new baseline.

We propose an automated method to detect, group and rectify arbitrarily-arranged coplanar repeated elements via energy minimization.

Supervising

• Rong Zou: Retrieval Robust to Object Motion Blur, Master thesis, Nov 2023.
• Yiming Zhao: Predicting 3D Shape and Texture of Fast Moving Cars, Semester project, May 2023, accepted to ICCV'23.
• Rajat Thakur: Retrieval Robust to Object Motion Blur, Semester project defended in March 2022.
• Adrian Klaeger: Temporal Super-Resolution of Multiple Fast-Moving Objects, Master thesis defended in September 2021, thesis, GitHub.
• Harish Rajagopal: Improving DeFMO With Learned Losses, Semester project defended in June 2021, report, GitHub.
• Julius Fricke: ADMM Algorithm Unrolling: Deblurring and Matting, Bachelor thesis defended in April 2021.

Reviewing

• Conferences: CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, 3DV, WACV.
• Journals: PAMI, IJCV.

Teaching

Adapted from here.