Performance Capture refers to the technology and processes involved in digitally recreating the movements, poses, gestures, appearance, and expressions of a human and its surroundings. Methods to do so typically use body-worn sensors, RGB and depth cameras, and advanced Computer Vision algorithms to record the intricate details of a performer's movements.

In this seminar we will discuss state-of-the-art literature on human-centric computer vision topics including but not limited to human pose estimation, hand and eye-gaze estimation as well as generative modeliing of detailed human activities.

The course provides an introduction to the field of human-computer interaction and focuses on role of the user in system design. Methods used to analyze the user experience will be introduced to show how they inform the design of new interfaces, systems, and technologies. Emerging methods and tools in computational interaction and optimization for UI design will also be introduced.

Recent developments in neural networks have drastically advanced the performance of machine perception systems in a variety of areas including computer vision, robotics, and human shape modeling.This course is a deep dive into deep learning algorithms and architectures with applications to a variety of perceptual and generative tasks.