Optical Mouse: 3D Mouse Pose From Single-View Video
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Abstract (arXiv)
We present a method to infer the 3D pose of mice, including the limbs and feet, from monocular videos. Many human clinical conditions and their corresponding animal models result in abnormal motion, and accurately measuring 3D motion at scale offers insights into health. The 3D poses improve classification of health-related attributes over 2D representations. The inferred poses are accurate enough to estimate stride length even when the feet are mostly occluded. This method could be applied as part of a continuous monitoring system to non-invasively measure animal health.
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References · 14
- 2014Microsoft COCO: Common Objects in Contextcited by 17,608
- 2015SSD: Single Shot MultiBox Detectorcited by 3,130
- 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,162
- 2016Stacked Hourglass Networks for Human Pose Estimationcited by 915
- 2016Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Imagecited by 695
- 2017Learning from Synthetic Humanscited by 359
- 2016Convolutional Pose Machinescited by 325
- 2018Learning 3D Human Dynamics from Videocited by 316
- 2018Learning to Estimate 3D Human Pose and Shape from a Single Color Imagecited by 226
- 2018Markerless tracking of user-defined features with deep learningcited by 206
- 2017Unite the People: Closing the Loop Between 3D and 2D Human Representationscited by 205
- 2016Composing graphical models with neural networks for structured representations and fast inferencecited by 113
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- 2019Exploiting temporal context for 3D human pose estimation in the wildcited by 110
- 2017Self-supervised Learning of Motion Capturecited by 97
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