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)
主持人:刘烨斌,清华大学(个人主页:http://liuyebin.com/
报告题目:On How to Capture and Learn Digital Human Models
报告摘要:
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.
讲者简介:
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.
讲者个人主页:http://virtualhumans.mpi-inf.mpg.de/

 

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观看直播的链接:http://webinar.games-cn.org

Liu, Ligang

刘利刚,中国科学技术大学教授、博导。分别于1996年及2001年于浙江大学获得应用数学学士及博士学位。从事计算机图形学研究,已在该领域顶级期刊ACM Transactions on Graphics上发表论文三十余篇。曾于微软亚洲研究院、浙江大学、哈佛大学工作或访问。曾获微软青年教授奖、陆增镛CAD&CG高科技奖一等奖、国家自然科学奖二等奖等奖项。任多个国内外会议的大会共同主席或论文共同主席及多个国际学术期刊编委。负责创建了中科大《计算机图形学前沿》暑期课程及CCF CAD&CG专委图形学在线交流平台GAMES。

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