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Edgar Tretschk

Max-Planck-Institut für Informatik
D4: Computer Graphics
3D Reconstruction
 office: Campus E1 4, Room 221
Saarland Informatics Campus
66123 Saarbrücken
Germany
 email: Get my email address via email
 phone: +49 681 9325-4021
 fax: +49 681 9325-4099
I'm a 1st year Ph.D. candidate in the Graphics, Vision and Video group supervised by Prof. Dr. Christian Theobalt.

Research Interests

  • Real-time tracking of general objects
  • 3D reconstruction of non-rigid objects
  • Machine learning for computer graphics/vision

Publications

DispVoxNets: Non-Rigid Point Set Alignment with Supervised Learning Proxies

Soshi Shimada, Vladislav Golyanik, Edgar Tretschk, Didier Stricker, Christian Theobalt

3DV 2019

We introduce a supervised-learning framework for non-rigid point set alignment of a new kind - Displacements on Voxels Networks (DispVoxNets) - which abstracts away from the point set representation and regresses 3D displacement fields on regularly sampled proxy 3D voxel grids. Thanks to recently released collections of deformable objects with known intra-state correspondences, DispVoxNets learn a deformation model and further priors (e.g., weak point topology preservation) for different object categories such as cloths, human bodies and faces.

[Project Page] [arXiv]



DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects

Edgar Tretschk, Ayush Tewari, Michael Zollhöfer, Vladislav Golyanik, Christian Theobalt

We propose a general-purpose DEep MEsh Autoencoder (DEMEA) which adds a novel embedded deformation layer to a graph-convolutional mesh autoencoder. We demonstrate multiple applications of DEMEA, including non-rigid 3D reconstruction from depth and shading cues, non-rigid surface tracking, as well as the transfer of deformations over different meshes.

[Project Page] [arXiv]



Sequential Attacks on Agents for Long-Term Adversarial Goals

Edgar Tretschk, Seong Joon Oh, Mario Fritz

2. ACM Computer Science in Cars Symposium 2018

We show that an adversary can be trained to control a deep reinforcement learning agent. Our technique works on fully trained victim agents and makes them pursue an alternative, adversarial goal when under attack. In contrast to traditional attacks on e.g. image classifiers, our setting involves adversarial goals that may not be immediately reachable but instead may require multiple steps to be achieved.

[pdf]



Teaching

  • Summer semester 2019:
    Supervisor for Computer Vision and Machine Learning for Computer Graphics Seminar, Saarland University and MPI for Informatics
  • September/October 2016, 2017, 2018:
    Coach for the Mathematik-Vorkurs für Informatiker (Math preparation course for new CS students), Saarland University
  • September/October 2017:
    Voluntary lecturer for the Mathematik-Vorkurs für Informatiker (Math preparation course for new CS students), Saarland University
  • Winter semester 2017/18:
    Tutor for Grundzüge der Theoretischen Informatik (Theoretical Computer Science), Lecturer: Prof. Dr. Markus Bläser, Saarland University
  • Winter semester 2015/16:
    Tutor for Programmierung 1 (Programming 1), Lecturer: Prof. Dr. Gert Smolka, Saarland University
  • March 2015:
    Tutor for re-exam preparation in Programmierung 1 (Programming 1), Lecturer: Prof. Bernd Finkbeiner, Ph.D., Saarland University

Recent Positions

Education

Awards & Honors

  • November 2017:
    Bachelor Award (for the best Bachelor graduates in CS)
  • April 2015 -- March 2017:
    Member of the Bachelor Honors Program (special support program for talented and ambitious Bachelor students in CS)
  • April 2015 -- March 2017:
    Deutschlandstipendium scholarship