CAREER: Theory and Practice of Bayesian Motion Synthesis

职业：贝叶斯运动综合的理论与实践

• 批准号: 1055046
• 负责人: Jinxiang Chai
• 金额: $ 40.33万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-03-15 至 2017-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1055046&HistoricalAwards=false
• 关键词: CAREER; Theory; Practice; Bayesian; Motion

This project develops the theory and algorithms for the next generation of statistical motion models and their applications in Bayesian motion synthesis. Thus far, one of the most effective ways to model human movement is to construct statistical motion models from prerecorded motion data. While the promise of learning from motion data is unlimited, current statistical motion modeling techniques suffer from four major limitations. Firstly, they lack scalability and ability to model large and heterogeneous datasets. Secondly, they do not capture environmental contact information embedded in prerecorded motion data. Thirdly, they are mainly focused on modeling spatial-temporal patterns within a small temporal window rather than the global motion structures of human actions and thus face great risk of destroying global motion structures in motion generalization. Lastly and most importantly, they do not consider dynamics that cause the motion. This project investigates a new generation of statistical motion models that address these four challenges. The project also develops new Bayesian motion synthesis algorithms that leverage the proposed generative models in graphics and vision applications. In addition, the research produces new animation modeling systems for novices, new performance interfaces for full-body motion control, and new technologies for video-based motion capture. In the project, the PI makes special efforts to recruit students from under-represented groups, to integrate the research into existing and new courses, and to use a high school competition as a channel for attracting more young students to pursue careers in computer science.

本计画旨在发展下一代统计运动模型的理论与演算法，并将其应用于贝氏运动合成。到目前为止，对人体运动建模的最有效的方法之一是从预先记录的运动数据中构建统计运动模型。虽然从运动数据中学习的前景是无限的，但当前的统计运动建模技术受到四个主要限制。首先，它们缺乏可扩展性和对大型异构数据集建模的能力。其次，它们不捕获嵌入在预先记录的运动数据中的环境接触信息。第三，它们主要集中在小时间窗口内的时空模式建模，而不是人类行为的全局运动结构，因此在运动泛化中面临着破坏全局运动结构的巨大风险。最后也是最重要的是，他们没有考虑导致运动的动力学。该项目研究了新一代的统计运动模型，以解决这四个挑战。该项目还开发了新的贝叶斯运动合成算法，该算法利用了图形和视觉应用中的生成模型。此外，这项研究还为新手提供了新的动画建模系统，为全身运动控制提供了新的性能接口，并为基于视频的运动捕捉提供了新技术。在该项目中，PI特别努力从代表性不足的群体中招收学生，将研究纳入现有和新课程，并利用高中竞赛作为吸引更多年轻学生从事计算机科学职业的渠道。