Fakultät Informatik

Master-Seminar – Machine Learning in Graphics

Lecturer

 Prof. Dr. Nils Thuerey,  Prof. Dr. Rüdiger Westermann,  Dr. Kiwon Um, and  Wei He

Studies

Master Informatics

Time, Place

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

Begin

16 October 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 topics in a talk (in English), which should last 30 minutes.
  • The semi-final 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

10 July 2017

Kick-off meeting in room MI 02.13.010 at 16:00

15 September 2017

Deadline for sending an e-mail with 3 preferences

22 September 2017

Notification of assigned paper

Papers

Date

Presenter

Paper

16 Oct 2017

Tobias Bernecker

2016, Dong et al.,  Image Super-Resolution Using Deep Convolutional Networks, IEEE Trans. Pattern Anal. Mach. Intell.

16 Oct 2017

Reza Roustaei Khoshkbijari

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

23 Oct 2017

Palle Klewitz

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

23 Oct 2017

Haoran Chen

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

30 Oct 2017

Maximilian Werhahn

2017, Yi et al.,  Learning Hierarchical Shape Segmentation and Labeling from Online Repositories, ACM Trans. Graph.

30 Oct 2017

Virendra Kumar Pathak

2017, Wang et al.,  O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis, ACM Trans. Graph.

06 Nov 2017

Hyeon Su Kim

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

06 Nov 2017

Artem Bishev

2017, Chu and Thuerey,  Data-driven Synthesis of Smoke Flows with CNN-based Feature Descriptors, ACM Trans. Graph.

13 Nov 2017

Tuba Topaloglu

2017, Li et al.,  Deep Extraction of Manga Structural Lines, ACM Trans. Graph.

13 Nov 2017

Christoph Neuhauser

2017, Gharbi et al.,  Deep Bilateral Learning for Real-time Image Enhancement, ACM Trans. Graph.

20 Nov 2017

Maria Dreher

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

20 Nov 2017

Felix Neumeyer

2017, Bako et al.,  Kernel-predicting Convolutional Networks for Denoising Monte Carlo Renderings, ACM Trans. Graph.

27 Nov 2017

Tobias Zengerle

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

27 Nov 2017

Max Heimbrock

2017, Holden et al.,  Phase-functioned Neural Networks for Character Control, ACM Trans. Graph.

04 Dec 2017

Luca Klingenberg

2017, Karras et al.,  Audio-driven Facial Animation by Joint End-to-end Learning of Pose and Emotion, ACM Trans. Graph.

04 Dec 2017

Hans Rauer

2017, Suwajanakorn et al.,  Synthesizing Obama: Learning Lip Sync from Audio, ACM Trans. Graph.

To download the paper, you can use the university access:
 https://www.ub.tum.de/en/eaccess

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.