16-726 Learning-Based Image Synthesis / Spring 2022

Time: Mondays, Wednesdays 3:05 pm - 4:25 pm ET

Location: remote-only for first 2 weeks / TBD later (see Canvas for Zoom links)

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 and VAEs). 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


  • Welcome to Learning-Based Image Synthesis! Please check out Canvas for our Zoom link.


Jun-Yan Zhu

junyanz at cs.cmu.edu

Teaching Assistants

Sheng-yu Wang

shengyu2 at andrew.cmu.edu

Zhiqiu Lin

zhiqiul at andrew.cmu.edu