16-726 Learning-Based Image Synthesis / Spring 2023

Time: Mondays, Wednesdays 9:30 am - 10:50 am ET

Location: NSH 1305

Horsehoe Bend


Course Description

This course introduces machine learning methods for image and video synthesis. The objectives of synthesis research vary from modeling statistical distributions of visual data, through realistic picture-perfect recreations of the world in graphics, and all the way to providing interactive tools for artistic expression. Key machine learning algorithms will be presented, ranging from classical learning methods (e.g., nearest neighbor, PCA, Markov Random Fields) to deep learning models (e.g., ConvNets, deep generative models, such as GANs, VAEs and Diffusion models). We will also introduce image and video forensics methods for detecting synthetic content. In this class, students will learn to build practical applications and create new visual effects using their own photos and videos.


This course requires familiarity with basic concepts of computer vision/graphics/image processing (16385 or 15462 or 15463 or 16720 or 18793). Some knowledge of machine learning (10301 or 10315 or 10601 or 10606 or 10607 or 10701) will also be helpful.

Previous Offerings


Jun-Yan Zhu

junyanz at cs.cmu.edu

Teaching Assistants

Nikos Gkanatsios

ngkanats at andrew.cmu.edu

Emily Kim

ekim2 at andrew.cmu.edu