GAMES Webinar 2018-71期（Human Performance Capture Tutorial-3）| Gerard Pons Moll（Max Planck for Informatics）
【GAMES Webinar 2018-71期（Human Performance Capture Tutorial-3）】
报告嘉宾：Gerard Pons Moll，Max Planck for Informatics
报告时间：2018年11月1日（星期四）晚8:00 – 9:30（北京时间）
报告题目：On How to Capture and Learn Digital Human Models
The world is shifting towards a digitization of everything — music, books, movies and news in digital form are common in our everyday lives. Digitizing human beings would redefine the way we think and communicate (with other humans and with machines), and it is necessary for many applications; for example, to transport people into virtual and augmented reality, for entertainment and special effects in movies, and for medicine and psychology.
Currently, digital people models typically lack realism or require time-consuming manual editing of physical simulation parameters. Our hypothesis is that better and more realistic models of humans and clothing can be learned directly by capturing real people using 4D scans, images, and depth and inertial sensors. Combining statistical machine learning techniques and geometric optimization, we create realistic statistical models from the captured data. To be able to digitize people from low-cost ubiquitous sensors (RGB cameras, depth or small number of wearable inertial sensors), we leverage the learned statistical models — which are robust to noise and missing data.
In this talk I will describe the main techniques and the mathematics necessary to learn and model digital humans from real captured people. I will show how to model human pose, shape, soft-tissue and clothing, as well as techniques to reconstruct detailed avatars from monocular video. I will conclude the talk outlining the next challenges in building digital humans and perceiving them from sensory data.
Gerard Pons-Moll is the head of the research group “Real Virtual Humans” at the Max Planck for Informatics (MPII) in Saarbrücken, Germany. His research lies at the intersection between computer vision, computer graphics and machine learning — with special focus on analyzing people in videos, and creating virtual human models by “looking” at real ones. His research has produced the most advanced statistical human body models of pose, shape, soft-tissue and clothing (which are currently used for a number of applications in industry and research), as well as pioneering algorithms to track and reconstruct 3D people models from images, video, depth, and IMUs.
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