Monocular Absolute 3D Human Pose Estimation with an Uncalibrated Fixed Camera

Atsunori Moteki, Yukio Hirai, Genta Suzuki, Hideo Saito

Abstract: In this paper, we propose a method for absolute 3D human pose estimation (HPE) with an uncalibrated monocular camera. In the case of analyzing workers’ movement with an existing uncalibrated camera, the previous method cannot estimate absolute human pose due to the lack of information on camera parameters. Our proposed method overcomes this limitation by determining the position and scale of humans based on the pose of surrounding objects. Specifically, we predict the intrinsic and extrinsic parameters of the camera through user-guided manual manipulation. Subsequently, the estimated human pose is transformed from local coordinates to global coordinates for each frame. This absolute coordinate representation allows for real-time prediction of human movements relative to objects. To assess the efficacy of our method, we conducted three kinds of experiments. A user study revealed that the proposed user-guided method archives accurate estimation of camera parameters. Quantitative evaluation using a public dataset demonstrated that our method can predict human pose with practical accuracy, providing a benchmark for future enhancements. Qualitative evaluation with a unique dataset showed that our method could easily generate digital twin representations across diverse environments and camera positions.