Master-Seminar – Deep Learning in Computer Graphics (IN2107, IN0014)

LecturerKiwon Um, Philipp Holl, and Nils Thuerey
StudiesMaster Informatics
Time, PlaceMondays 16:00-18:00, Seminarraum MI 02.13.010
Begin15 October 2018

Content

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

Requirements

Attendance
  • It is only allowed to miss a single time-slot. Missing a second one means failing the seminar. If you have to miss any, please let us know in advance.
Presentation (slides)
  • You will present your topic in English, and the talk should last 30 minutes. After that, a discussion session for ca. 10 minutes will follow.
  • The slides should be structured according to your presentation. You can use any layout or template you like.
  • Plagiarism is important; please do not simply copy the original authors' slides. You can certainly refer to them.
  • The semi-final slides (PDF) should be sent one week before the talk; otherwise, the talk will be canceled.
  • We strongly encourage you to finalize the semi-final version as far as possible. We will take a look at the version and give feedback. You can revise your slides until your presentation.
  • Be ready in advance. We suggest testing the machines you are going to use before the lecture starts. You can bring your laptop or ask us one (also any converter you need for the projector) in advance. A laser pointer will be provided, so you can use if you want.
Report

  • A short report (4 pages max. excluding references in the ACM SIGGRAPH TOG format (acmtog) - you can download the precompiled latex template) should be prepared and sent within two weeks after the talk, i.e., by 23:59 on Monday. When you send the report, please send the final slides (PDF) together.
  • Guideline: You can begin with writing a summary of the work you present as a start point; but, it would be better if you focus more on your own research rather than just finishing with the summary of the paper. We, including you, are not interested in revisiting the work done before; it is more meaningful if you make an effort to put your own reasoning about the work, such as pros and cons, limitation, possible future work, your own ideas for the issues, etc.

Preliminary Schedule

18 September 2018 Deadline for sending an e-mail with 3 preferences
21 September 2018 Notification of assigned paper
15 October 2018 Preparation week: No lecture!
22 October 2018 The first talk

Papers

DatePresenterPaper
22 Oct 2018Sandra Grujovic 2017, Bako et al., Kernel-predicting Convolutional Networks for Denoising Monte Carlo Renderings, ACM Trans. Graph.
22 Oct 2018Michael Sorg 2017, Dahl et al., Pixel Recursive Super Resolution, arXiv.org
CanceledJames Li 2017, Wang et al., O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis, ACM Trans. Graph.
29 Oct 2018Peng Chen 2017, Yi et al., Learning Hierarchical Shape Segmentation and Labeling from Online Repositories, ACM Trans. Graph.
05 Nov 2018Alexander Potapov2014, Goodfellow et al., Generative Adversarial Networks, Advances in Neural Information Processing Systems 27
05 Nov 2018Markus Gögele 2016, Ruder et al., Artistic Style Transfer for Videos, GCPR
(optional) 2015, Gatys et al., A Neural Algorithm of Artistic Style, arXiv.org
12 Nov 2018Simon Anlauff 2015, Mnih et al., Human-Level Control Through Deep Reinforcement Learning, Nature
CanceledFlorian Dollinger2017, Gharbi et al., Deep Bilateral Learning for Real-time Image Enhancement, ACM Trans. Graph.
19 Nov 2018Luca Sinn 2017, Li et al., Deep Extraction of Manga Structural Lines, ACM Trans. Graph.
19 Nov 2018Johannes Rohwer 2018, Simo-Serra et al., Mastering Sketching: Adversarial Augmentation for Structured Prediction, ACM Trans. Graph.
26 Nov 2018Felix Trost 2016, Peng et al., Terrain-adaptive Locomotion Skills Using Deep Reinforcement Learning, ACM Trans. Graph.
26 Nov 2018Dirk Münzenmaier 2017, Holden et al., Phase-functioned Neural Networks for Character Control, ACM Trans. Graph.
03 Dec 2018Henri Rößler 2017, Suwajanakorn et al., Synthesizing Obama: Learning Lip Sync from Audio, ACM Trans. Graph.
03 Dec 2018Timm Knörle 2018, Kim et al., Deep Video Portraits, ACM Trans. Graph.
10 Dec 2018Jakob Englhauser 2017, Karras et al., Audio-driven Facial Animation by Joint End-to-end Learning of Pose and Emotion, ACM Trans. Graph.
10 Dec 2018Jean Vieira Filho2018, Bailey et al., Fast and Deep Deformation Approximations, ACM Trans. Graph.

You can access the papers through TUM library's eAccess.

References