**Time and location:** TR 11:10 - 12:25 in 126 HRBB

**Office hours:** T 10:00 - 11:00

**Office location:** 527B HRBB

**Email:** nimak@tamu.edu

In this course, we first cover the basic principles of deep learning, including different network architectures and training strategies. We then study the recent use of deep learning in a variety of computer graphics applications such as view synthesis, image inpainting, colorization, and Monte Carlo denoising. Specifically, we discuss different solutions for this challenging problem, including novel system designs and dataset preparation strategies.

There are no prerequisites. However, basic understanding of machine learning and computer graphics is recommended. Basic knowledge of Python is also recommended, but not necessary.

This is a graduate level course and it mainly consists of students presenting recent research papers on the applications of deep learning in computer graphics. For the first few classes, the instructor covers the basics of deep learning. Afterwards, each class consists of two presentations by students. Each presentation should be 20 minutes with an extra 15 minutes for class discussion.

We will have two assignments, which need to be implemented in one of the deep learning packages (PyTorch, TensorFlow, etc.). We will also have a final project, which could be anything related to the use of deep learning in computer graphics.

There is no textbook for this course, since it is based on recent research papers. The papers are all available online.

**Presentations (20%):**Students are expected to present recent research papers during the semester.**Attendance and participation (10%):**This is a graduate level course and, thus, students are expected to attend the class and participate in the discussions. Absences will be handled according to student rule 7. For more information click here.**Programming assignments (30%):**There will be 2 programming assignments for this course.**Final project (40%):**Students are free to choose any project that addresses a computer graphics application using deep learning. They can work on the project individually or in teams. Students will propose the project at the middle of the semester. The final project presentation as well as a report will be due at the end of the semester.

Date | Topic | Papers | Presenter(s) | Assignments |
---|---|---|---|---|

Aug 28 | Introduction and overview | Nima | ||

Aug 30 | Basics of deep learning I | Nima | ||

Sep 4 | Basics of deep learning II | Nima | ||

Sep 6 | Style transfer | Gatys 16, Ruder 16 | Tushar, Duc | |

Sep 11 | Image synthesis I | Radford 15, Isola 17 | Prateek, Nitin | |

Sep 13 | Image synthesis II | Chen 17, Qi 18 | Saurabh, Qinbo | |

Sep 18 | View Synthesis I | Zhou 16, Flynn 16 | Nishant, Avinash | Hw 1 out |

Sep 20 | View Synthesis II | Kalantari 16, Wu 17 | Avinash, Yaohua | |

Sep 25 | Colorization I | Larsson 16, Lizuka 17 | Buvaneish, Buvaniesh | |

Sep 27 | Colorization II | Zhang 17, He 18 | Stuti, Peng | |

Oct 2 | High dynamic range imaging I | Kalantari 17, Wu 18 | Duc, Yaohua | Hw 1 due |

Oct 4 | High dynamic range imaging II | Eilertsen 17, Endo 17 | Stuti, Saurabh | |

Oct 9 | Image inpainting | Pathak 16, Lizuka 17 | Yerania, Yashwanth | Hw 2 out |

Oct 11 | Image super-resolution | Dong 14, Ledig 17 | Prateek, Samia | |

Oct 16 | Sketches I | Wang 15, Sangkloy 17 | Yerania, Fieza | |

Oct 18 | Sketches II | Sangkloy 17, Simo-Serra 16 | Fieza, Peng | |

Oct 23 | Final project proposals I | |||

Oct 25 | Final project proposals I | Hw 2 due | ||

Oct 30 | Intrinsic decomposition I | Narihira 15, Shi 17 | Cassandra, Cassandra | |

Nov 1 | Intrinsic decomposition II | Liu 17, Bi 18 | Yashwanth, Peeyush | |

Nov 6 | Monte Carlo denoising I | Kalantari 15, Bako 17 | Logan, Brennen | |

Nov 8 | Monte Carlo denoising II | Chaitanya 17, Vogels 18 | Nitin, Brennen | |

Nov 13 | Shading | Nalbach 17, Thomas 17 | Kevin, Logan | |

Nov 15 | Relighting | Ren 13, Ren 15 | Xiaojing, Tushar | |

Nov 20 | Image denoising | Gharbi 16, Mildenhall 18 | Xiaojing, Peeyush | |

Nov 22 | Thanksgiving - no class | |||

Nov 27 | Other topics I | Zhou 18, Xu 18 | Kevin, Samia | |

Nov 29 | Other topics II | Ephrat 18, Prashnani 18 | Qinbo, Nishant | |

Dec 4 | Final project presentation I | |||

Dec 6 | Final project presentation II |

*Schedule is subject to small changes.