Description
This course introduces deep learning methods and AI technologies applied to four main areas of Computer Graphics: rendering, geometry, animation, and computational photography. We will study a wide range of problems in content creation for images, shapes, and animations, recently advanced by deep learning techniques. For each problem, we will understand its conventional solutions, study the state-of-the-art learning-based approaches, and critically evaluate their results as well as the impacts on researchers and practitioners in Computer Graphics. The topics include differentiable rendering/neural rendering, BRDF estimation, texture synthesis, denoising, procedural modeling, mesh segmentation, view prediction, colorization, style transfer, sketch simplification, character animation, physics simulation, and facial animation. Through programming projects and homework, students who successfully complete this course will be able to use neural rendering algorithms for image manipulation, apply neural procedural modeling for shape and scene synthesis, implement policy learning algorithms for creating character animation, and exploit data-driven methods for simulating physical phenomena.
Prerequisites: CS229, CS231N, or an equivalent intro machine learning course. CS148/248 is recommended but not required.
Announcement: Winter 2024 students, please join Ed for discussion.
Grading:- Paper presentations: 48%
- Discussions: 10%
- Quiz: 12%
- Final project: 30%