This paper introduces V-SysId, a novel method that enables simultaneous keypoint discovery, 3D system identification, and extrinsic camera calibration from an unlabeled video taken from a static camera, using only the family of equations of motion of the object of interest as weak supervision. V-SysId takes keypoint trajectory proposals and alternates between maximum likelihood parameter estimation and extrinsic camera calibration, before applying a suitable selection criterion to identify the track of interest. This is then used to train a keypoint tracking model using supervised learning. Results on a range of settings (robotics, physics, physiology) highlight the utility of this approach.
翻译:本文介绍了V-SysId这一新的方法,它使得能够同时发现关键点,3D系统识别,以及从静态相机拍摄的未贴标签的视频中校准外部相机,仅使用受关注对象运动方程式的组合,作为薄弱的监管。V-SysId采用关键点轨迹建议和在最大可能性参数估计和外部相机校准之间的替代方法,然后适用适当的选择标准来确定兴趣轨迹。然后用它来利用监督的学习来培训关键点跟踪模型。关于一系列设置(机器人、物理、生理学)的结果突出这一方法的实用性。