Vorlesung: Advanced Deep Learning for Graphics


  • Dozent(en):
  • Beginn: 22.04.2020
  • Zeiten: Wed. 12:15 - 13:45, tbd
  • Veranstaltungsnummer: MA-INF 2217
  • Studiengang: Master
  • Aufwand: 4.0 SWS
  • Prüfungen: 28 July 2020 (room reservation: 12:00 - 15:00) and 28 September 2020 (room reservation: 13:00 - 16:00)


  • Betreuer:
  • Beginn: TBA via mailing list
  • Zeiten: see BASIS and via appointment (with Soumajit Majumder)


This course focuses on cutting-edge Deep Learning techniques for computer graphics. After a brief review of CNNs the focus will be laid on autoencoders, generative models and the extension of these methods to graph- and manifold-structured data.  Applications discussed will include inverse problems in computer graphics and the synthesis of models including data completion and super-resolution.

The course will build upon the basics of machine learning as well as fundamentals and basic architectures of neural networks. Therefore, it is highly recommended to have taken Deep Learning for Visual Recognition or a similar course as a prerequisite. Exercises will be a mix of theory and practical (Python).





Übung 1: Introduction
Übungsblatt  (PDF-Dokument, 288 KB)
Übung 2: RNNs and GANs
Übungsblatt  (PDF-Dokument, 146 KB)
Übung 3: Image-to-Image Translation
Übungsblatt  (PDF-Dokument, 2.0 MB)
Übung 4: Optical Material Properties and 3D Data
Übungsblatt  (PDF-Dokument, 50 KB)
Übung 5: PointNet
Übungsblatt  (PDF-Dokument, 77 KB)

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