Markerless three-dimensional (3D) pose estimation has become an indispensable tool for kinematic studies of laboratory animals. Most current methods recover 3D poses by multi-view triangulation of deep network-based two-dimensional (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 macaques, and in circumstances where 3D triangulation is impractical or impossible. Our framework achieves accurate lifting for stereotypical and nonstereotypical behaviors from different camera angles. Thus, LiftPose3D permits high-quality 3D pose estimation in the absence of complex camera arrays and tedious calibration procedures and despite occluded body parts in freely behaving animals.

无标记三维 (3D) 姿态估计已成为实验室动物运动学研究不可或缺的工具。大多数当前方法通过基于深度网络的二维 (2D) 姿势估计的多视图三角测量来恢复 3D 姿势。然而，三角测量需要多个同步相机和复杂的校准协议，这阻碍了其在实验室研究中的广泛采用。在这里，我们描述了 LiftPose3D，这是一种基于深度网络的方法，它通过从单个 2D 摄像机视图重建 3D 姿势来克服这些障碍。我们通过将 LiftPose3D 应用到使用苍蝇、小鼠、大鼠和猕猴的多个实验系统以及在 3D 三角测量不切实际或不可能的情况下来说明 LiftPose3D 的多功能性。我们的框架从不同的摄像机角度实现了对刻板印象和非刻板印象行为的准确提升。因此，LiftPose3D 允许在没有复杂的相机阵列和繁琐的校准程序的情况下进行高质量的 3D 姿势估计，尽管自由行为动物的身体部位被遮挡。