Optical Mouse: 3D Mouse Pose From Single-View Video
This enables non-invasive, scalable monitoring of animal health for clinical research, though it is incremental as it builds on existing pose estimation methods.
The paper tackles the problem of inferring 3D mouse pose from single-view video, achieving accurate enough results to estimate stride length even with occluded feet and improving classification of health-related attributes over 2D representations.
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.