Fakultät Informatik

Master-Seminar – Machine Learning in Graphics

Lecturer

 N. Thuerey,  R. Westermann,  M. Niessner,  Kiwon Um

Studies

Master Informatics

Time, Place

Mondays 16:00-18:00, Seminarraum MI   02.13.010

Begin

24.04.2017

Content

In this course, students will autonomously investigate recent research about machine learning techniques in the computer graphics area. Independent investigation for further reading, critical analysis, and evaluation of the topic are required.

Requirements

  • The participants have to present their results in a talk, which should last 30 minutes. Talks will be given in English.
  • The slides (PDF) should be sent one week before the talk; otherwise, the talk will be canceled.
  • A short report (approximately 3-4 pages in  the ACM SIGGRAPH TOG format) should be prepared and sent within two weeks after the talk.
  • When you send the report, please send the final slides (PDF) together.

Preliminary Schedule

24.03.2017

Deadline for sending an e-mail with 3 preferences

31.03.2017

Notification of assigned paper

Papers

Date

Presenter

Paper

24 Apr 2017

Michael

2016, Dong et al.,  Image Super-Resolution Using Deep Convolutional Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence

24 Apr 2017

Thomas

2017, Dahl et al.,  Pixel Recursive Super Resolution, arXiv.org

01 May 2017

No talk

May Day (Maifeiertag)

08 May 2017

Elisabeth

2016, Yan et al.,  Automatic Photo Adjustment Using Deep Neural Networks, ACM Trans. Graph.

08 May 2017

Jonas

2015, Gryka et al.,  Learning to Remove Soft Shadows, ACM Trans. Graph.

15 May 2017

Julius

2014, Goodfellow et al.,  Generative Adversarial Networks, Advances in Neural Information Processing Systems 27 (NIPS 2014)

15 May 2017

Simon

2016, Ruder et al.,  Artistic Style Transfer for Videos, arXiv.org
(optional) 2015, Gatys et al.,  A Neural Algorithm of Artistic Style, arXiv.org

22 May 2017

Anna

2015, Kalantari et al.,  A Machine Learning Approach for Filtering Monte Carlo Noise, ACM Trans. Graph.

22 May 2017

Hans Theobald

2016, Ren et al.,  Image Based Relighting Using Neural Networks, ACM Trans. Graph.

29 May 2017

Oliver Jamal

2017, Zheng and Zheng,  NeuroLens: Data-Driven Camera Lens Simulation Using Neural Networks, Computer Graphics Forum

29 May 2017

Moritz

2015, Ladický et al.,  Data-driven Fluid Simulations Using Regression Forests, ACM Trans. Graph.

05 Jun 2017

No talk

Whit Monday (Pfingstmontag)

12 Jun 2017

Eric

2017, Kim et al.,  Category-Specific Salient View Selection via Deep Convolutional Neural Networks, Computer Graphics Forum

12 Jun 2017

Lukas

2015, Mnih et al.,  Human-Level Control Through Deep Reinforcement Learning, Nature

19 Jun 2017

Sebastian

2015, Guo et al.,  3D Mesh Labeling via Deep Convolutional Neural Networks, ACM Trans. Graph.

19 Jun 2017

Benedikt

2016, Simo-Serra et al.,  Learning to Simplify: Fully Convolutional Networks for Rough Sketch Cleanup, ACM Trans. Graph.

26 Jun 2017

Gerhard‑Mathias

2016, Nishida et al.,  Interactive Sketching of Urban Procedural Models, ACM Trans. Graph.

26 Jun 2017

Florian

2016, Zeng et al.,  3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions, arXiv.org

03 Jul 2017

Niklas

2016, Peng et al.,  Terrain-adaptive Locomotion Skills Using Deep Reinforcement Learning, ACM Trans. Graph.

03 Jul 2017

Jan

2016, Holden et al.,  A Deep Learning Framework for Character Motion Synthesis and Editing, ACM Trans. Graph.

References

 

News

Matthias Niessner, our new Professor from Stanford University, offers a number of interesting topics for  master theses.

 

PhD positions on   Computational Fabrication and 3D Printing and  Photorealistic Rendering for Deep Learning and Online Reconstruction are available at the Computer Graphics & Visualization group.