RI: Small: Acquisition and Modeling of Dense Nonrigid Shape and Motion

RI：小：密集非刚性形状和运动的采集和建模

• 批准号: 0916441
• 负责人: Charles Dyer
• 金额: $ 30.83万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916441&HistoricalAwards=false
• 关键词: RI; Small; Acquisition; Modeling; Dense

The objective of this project is to advance the state-of-the-art in acquiring and modeling dynamic non-rigid objects. The specific examples of non-rigid objects that the project is to focus on include: human faces, hands, soft tissues, cloths, and animals. The PI seeks to address the following two fundamental questions: (1) How can non-contact optical methods be used to measure dense 3D surface motion without physically modifying the appearance of the surface? (2) What physical and/or biological properties can be inferred from the acquired dense 3D motion data?The research team addresses these two questions by two simple but general ideas, namely the space-time approach and data-driven models. The space-time approach builds upon space-time stereo, and enables accurate optical measurements of 3D surface motion, as well as automatic registration of shape sequences among different dynamic objects. The data-driven models are used for both material recognition and deformation-EMG correlation. The project has a wide range of scientific impacts, including generating data for 2D face alignment and 3D face recognition in biometrics, generating data for 3D face emotion recognition in human computer interaction, measuring human body deformation in biomechanics, modeling soft tissues for orthopedics and computer-aided surgery, and building virtual human models for entertainment and education. These scientific impacts translate into benefits to society, for example, by building more accurate biometric systems to secure our country, innovating surgery procedure to reduce health insurance cost, and creating 3D digital replicas of great teachers to make our education available anywhere, anytime, at a lower cost.

该项目的目标是提高动态非刚性物体获取和建模的最先进水平。该项目关注的非刚性物体的具体例子包括：人脸、手、软组织、衣服和动物。该 PI 旨在解决以下两个基本问题：(1) 如何使用非接触式光学方法来测量密集的 3D 表面运动而不物理修改表面的外观？ （2）从获取的密集3D运动数据中可以推断出哪些物理和/或生物特性？研究团队通过两个简单但通用的想法解决了这两个问题，即时空方法和数据驱动模型。时空方法建立在时空立体的基础上，能够对 3D 表面运动进行精确的光学测量，并自动注册不同动态物体之间的形状序列。数据驱动模型用于材料识别和变形-肌电图关联。该项目具有广泛的科学影响，包括为生物识别中的2D人脸对齐和3D人脸识别生成数据、为人机交互中的3D人脸情感识别生成数据、测量生物力学中的人体变形、为骨科和计算机辅助手术建模软组织，以及为娱乐和教育构建虚拟人体模型。这些科学影响转化为社会效益，例如，通过建立更准确的生物识别系统来保护我们的国家，创新手术程序以降低健康保险成本，以及创建伟大教师的 3D 数字复制品，使我们可以随时随地以更低的成本接受教育。