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

Lecturer Nilam Tathawadekar, You XieProf. Dr. Nils Thürey,
Studies Master Informatics
Time, Place

Mondays 16:00-18:00, Online

Begin 25th., October 2021

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

Participants are required to first read the assigned paper and start writing a report. This will help you prepare for your presentation.

Attendance
  • It is only allowed to miss two talks. If you have to miss any, please let us know in advance, and write a one-page summary about the paper in your own words. Missing the third one means failing the seminar. 
  • As the seminar is completely online, we shall ask you for a short feedback or some comments for each talk. The feedback summary is just for checking attendance, so it doesn't need to be long. A few sentences will be good enough.
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 two weeks before the talk, i.e., by 23:59 on Monday.
  • 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.
  • For questions regarding your paper or feedback for a semi-final version of your report you can contact your advisor.
Presentation (slides)
  • You will present your topic in English, and the talk should last 30 minutes. After that, a discussion session of about 10 minutes will follow.
  • The slides should be structured according to your presentation. You can use any layout or template you like, but make sure to choose suitable colors and font sizes for readability.
  • Plagiarism should be avoided; 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. As the seminar in this semester is completely online, giving a virtual talk may be different from a real speech to your audience. To get prepared for your talk with "BigBlueButton", please read this guide by Lukas Prantl.
  • The final slides should be sent after the talk.

Schedule

16-Aug-21 Deregistration due
17-Sept-21 Deadline for sending an e-mail with 3 preferred topics
  Notification of assigned paper
20-Sept-21 Introduction lecture and Notification of assigned paper
  First talk

Topics

Name

Paper

Presentation Date

Advisor

Manxi Sun

2019, Chu et al., Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation, arXiv.org

8.11.21

You Xie

Giovanna Elizabeth Barra Mostajo

2019, Meka et al., Deep Reflectance Fields - High-Quality Facial Reflectance Field Inference From Color Gradient Illumination, ACM Trans. Graph

8.11.21

Nilam T

Alexander Epple

2019, Hermosilla et al., Deep-learning the Latent Space of Light Transport, arXiv.org

15.11.21

Nilam T

Francesco Verdini

2019, Werhahn et al., A Multi-Pass GAN for Fluid Flow Super-Resolution, ACM Comput. Graph. Interact. Tech.

15.11.21

You Xie

Arian Bajrami

2019, Thies et al., Deferred Neural Rendering: Image Synthesis using Neural Textures, arXiv.org

22.11.21

You Xie

Vladyslav Marchenko

2020, Dupont et al., Equivariant Neural Rendering, ICML

22.11.21

Nilam T

Egemen Kopuz

2020, Luo et al., Consistent Video Depth Estimation, ACM Trans. Graph

29.11.21

You Xie

Benjamin Holl

2019, Choi & Kweon, Deep Iterative Frame Interpolation for Full-frame Video Stabilization, arXiv.org

29.11.21

Nilam T

Veton Basoli

2019, Frühstück et al., TileGAN: Synthesis of Large-Scale Non-Homogeneous Textures, arXiv.org

6.12.21

Nilam T

Paula-Henriette Herold

2021, Wang et al.,Rethinking and Improving the Robustness of Image Style Transfer, CVPR

6.12.21

You Xie

Haowei Xuan

2021, Wu et al., Coarse-to-Fine: Facial Structure Editing of Portrait Images via Latent, Space Classifications, ACM Trans. Graph

13.12.21

You Xie

Marcel Südmeyer

2020, Wang et al., Attribute2Font: Creating Fonts You Want From Attributes, ACM Trans. Graph

13.12.21

Nilam T

Umesh Rajesh Ramchandani

2021, Chu et al., Learning Meaningful Controls for Fluids, ACM Trans. Graph

20.12.21

You Xie

Anna Ribic

2020, Xiao et al., Neural Supersampling for Real-Time Rendering, ACM Trans. Graph.

20.12.21

Nilam T

Zumrud Shukurlu

2021, Yin et al., Learning to Recover 3D Scene Shape from a Single Image, CVPR

11.1.22

Nilam T

Marc Gavilan Gil

2020, Kopf et al., One Shot 3D Photography, ACM Trans. Graph

11.1.22

You Xie

References