【学术报告会】浙江大学周晓巍、王高昂教授学术报告会通知

报告会时间:5.7(周五)2:00pm

报告会地点:1教109党员之家

报告主题:周晓巍教授:Human Motion Capture from RGB Videos

                    王高昂教授:Towards the Challenges in Multi-Object Tracking

欢迎广**生参会学习交流!

Human Motion Capture from RGB Videos

Human motion capture (MoCap) is the basis of many human-centered applications such as human-computer interaction, AR/VR, sports ****ysis, healthcare, etc. Existing commercial MoCap systems are often expensive and only usable in well-controlled environments. How to make MoCap systems more lightweight and widely applicable is a long-standing problem in computer vision and graphics communities. In this talk, I will introduce our recent efforts towards this goal, which aim to recover 3D human motion with only RGB videos as input. Specifically, I will discuss how to estimate 3D human poses from multi-view or single-view images, recover high-quality human motion from unsynchronized internet videos, and synthesize free-view videos (bullet time) of dynamic humans from sparse multi-view videos. 

周晓巍,浙江大学“百人计划”研究员、博士生导师。2008年本科毕业于浙江大学,2013年博士毕业于香港科技大,2014至2017年在宾夕法尼亚大学 GRASP 机器人实验室从事博士后研究。2017年入选国家级青年项目并加入浙江大学。研究方向主要为计算机视觉及其在混合现实、机器人等领域的应用,在3D目标检测、姿态估计、运动捕捉、图像匹配等方面取得了一系列成果,相关工作曾多次获得CVPR及ICCV等顶级会议口头报告,曾获得CVPR18 3DHUMANS Workshop Best Poster Award、CVPR19 Best Paper Finalists、“陆增镛CAD&CG高科技奖”一等奖。担任CVPR21和ICCV21领域主席。更多信息请见个人主页:xzhou.me

 

Towards the Challenges in Multi-Object Tracking

Multi-object tracking (MOT) has drawn great attention in recent years. This technique is critically needed in many tasks, such as traffic flow ****ysis, human behavior prediction, autonomous driving assistance, etc. MOT has achieved great improvement over the past few years. However, some challenges remain, such as sensitiveness to occlusion, instability for multi-modality fusion, and non-robustness to cross-view perception. In this talk, I will present our recent works towards the challenges in MOT. Specifically, I will introduce how to exploit motion patterns in long-term tracking, how to deal with association errors, and how to combine geometry constraints in the cross-view setting.

王高昂,浙江大学国际联合学院研究员,UIUC**助理教授,博士生导师。2013年本科毕业于复旦大学,2015年硕士毕业于威斯康星麦迪逊分校,2019年博士毕业于华盛顿大学电子与计算机工程,之后工作于旷视北美研究院和Wyze Labs,2020年9月加入浙江大学。主要研究方向有计算机视觉,机器学习,图像和视频处理,具体还包括多目标跟踪,姿态估计,主动学习等。在IJCAI,CVAUI担任程序委员会成员,在许多国际著名期刊及会议担任审稿人角色。自2017年起,连续三年参加了英伟达(Nvidia)组织的智慧城市挑战赛,总计在5个赛道中取得4次第一1次第二的名次。2021年依托国际会议ICMR与华盛顿大学联合组织举办雷达目标检测挑战赛ROD2021。2020年荣获“创新嘉兴精英引领计划”领军人才项目。更多信息见个人主页:https://person.zju.edu.cn/gaoangwang