G&V: Medium: Collaborative Research: Contact-Based Human Motion Acquisition and Synthesis

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• 批准号: 1064983
• 负责人: Karen Liu
• 金额: $ 38.48万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-01 至 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1064983&HistoricalAwards=false
• 关键词: G& Medium; Collaborative; Research; Contact

To date, motion capture technologies suffer from three major limitations. First, the hardware devices are restrictive, cumbersome, and expensive. Second, most techniques only record the kinematic information of the movement, rather than underlying dynamic properties or control mechanisms. Third, the current technique fails to capture the interaction between the subject and the environment. Without the information of contacts, reconstructing motion that consists of complex contact phenomena is nearly impossible. This project develops a new motion acquisition and reconstruction technique that solves all three problems aforementioned. The new technique combines the force sensors and a single video camera to reconstruct full-body poses, joint torques, and contact forces in an unconstrained setting. In contrast to expensive lab equipment, the proposed system consists of a pair of low-cost, non-intrusive force-sensing shoes and a single consumer-level video camera that can be used to acquire motions difficult to capture in the lab. This acquisition technology enables new design of motion controllers by leveraging a large amount of real-world contact data. The research also develops new data representations and novel algorithms for intelligent and efficient motion planning and evaluates the developed motion controllers by simulating a human figure performing challenging balanced activities in a novel and unpredicted environment. The project is tightly integrated with education components in both Georgia Tech and Texas A&M. The research of this project lends itself well to solve important real-world problems for computer graphics. The results from this project would impact research in video gaming, sports training, remote health care, biped robots, and virtual characters, etc.

迄今为止，动作捕捉技术面临三个主要限制。首先，硬件设备受限、笨重且昂贵。其次，大多数技术仅记录运动的运动学信息，而不记录底层的动态属性或控制机制。第三，当前的技术无法捕捉主体与环境之间的相互作用。如果没有接触信息，重建由复杂接触现象组成的运动几乎是不可能的。该项目开发了一种新的运动采集和重建技术，可以解决上述所有三个问题。这项新技术结合了力传感器和单个摄像机，可以在不受约束的环境中重建全身姿势、关节扭矩和接触力。与昂贵的实验室设备相比，所提出的系统由一双低成本、非侵入式力感应鞋和一个消费级摄像机组成，可用于获取实验室中难以捕捉的运动。这种采集技术通过利用大量真实世界的接触数据，实现运动控制器的新设计。该研究还开发了新的数据表示和新颖的算法，用于智能高效的运动规划，并通过模拟人体在新颖且不可预测的环境中执行具有挑战性的平衡活动来评估所开发的运动控制器。该项目与佐治亚理工学院和德克萨斯农工大学的教育部分紧密结合。该项目的研究非常适合解决计算机图形学的重要现实问题。该项目的成果将影响视频游戏、运动训练、远程医疗、双足机器人和虚拟角色等领域的研究。