Luiz Velho
Warren Weaver Hall
251 Mercer Street (room 407)
Contact: lv776@nyu.edu
Subject: “AI Graphics 2024”
Mondays, 2:00 PM - 4:00 PM, CIWW 317
Keru Wang
Contact: kw2727@nyu.edu
Haitian Jiang
Contact: hj2533@nyu.edu
Office Hours
(starting from January 29th):
This course introduces deep learning methods for digital media, including modern advances in 3D Graphics and Vision. We will cover the fundamental concepts in Image Processing, Image Analysis, Geometric Modeling, and Visualization. We will then study Machine Learning techniques applied to 3D tasks at object and scene level.
At the end of this course, you will be familiar with the following topics: machine learning principles for analysis and syntesis of media signals; basic notions of geometry in graphics and vision; fundamentals of Fourier theory and Multiscale models; representations of images, three dimensional objects and scenes using deep neural networks; and applications of differentiable rendering for self-supervised learning of 3D scene properties.
This course will consist of lectures & discussions. Along with this there would be 2 mandatory coding assignments that will account for 80% of the grade. There will also be 1 optional assignment. In-Class participation and a Final Exam will account for 20% of the grade.
You need to be comfortable with: introductory machine learning concepts, linear algebra, basic multivariable calculus, intro to probability. You also need to have programming skills in Python. Optional, but recommended: experience with neural networks, introductory-level familiarity with computer vision. Note: if you don't meet all the prerequisites above please contact the instructor by email.