Markerless 3D pose estimation has become an indispensable tool for kinematic studies of laboratory animals. Most current methods recover 3D pose by multi-view triangulation of deep network-based 2D pose estimates. However, triangulation requires multiple, synchronized cameras and elaborate calibration protocols that hinder its widespread adoption in laboratory studies. Here, we describe LiftPose3D, a deep network-based method that overcomes these barriers by reconstructing 3D poses from a single 2D camera view. We illustrate LiftPose3D's versatility by applying it to multiple experimental systems using flies, mice, rats, and macaque monkeys and in circumstances where 3D triangulation is impractical or impossible. Our framework achieves accurate lifting for stereotyped and non-stereotyped behaviors from different camera angles. Thus, LiftPose3D permits high-quality 3D pose estimation in the absence of complex camera arrays, tedious calibration procedures, and despite occluded body parts in freely behaving animals.

中文翻译：

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LiftPose3D，一种基于深度学习的方法，可将实验动物的2D姿势转换为3D姿势

无标记3D姿势估计已成为实验室动物运动学研究中必不可少的工具。当前大多数方法都是通过对基于深度网络的2D姿势估计进行多视图三角剖分来恢复3D姿势。但是，三角剖分需要多个同步的摄像机和复杂的校准协议，这阻碍了三角剖分在实验室研究中的广泛采用。在这里，我们介绍LiftPose3D，这是一种基于深度网络的方法，通过从单个2D摄像机视图重建3D姿势来克服这些障碍。我们通过将LiftPose3D应用于苍蝇，小鼠，大鼠和猕猴的多个实验系统，以及在3D三角剖分不可行或不可能的情况下，说明了它的多功能性。我们的框架可从不同的摄影机角度实现对定型和非定型行为的精确提升。因此，