CVPR2023 – 3D Human Pose Estimation via Intuitive Physics

In this episode we discuss 3D Human Pose Estimation via Intuitive Physics
by Shashank Tripathi, Lea Müller, Chun-Hao P. Huang, Omid Taheri, Michael J. Black, Dimitrios Tzionas. This paper introduces a method called IPMAN (Intuitive Physics-based Human Pose Estimation) that aims to estimate 3D human pose from images while producing physically plausible body configurations. The method leverages intuitive-physics terms to infer the pressure heatmap on the body, the center of pressure (CoP), and the body’s center of mass (CoM) to encourage floor contact and overlapping CoP and CoM. The proposed method is evaluated on standard datasets and a new dataset with complex poses and body-floor contact, showing improved accuracy compared to state-of-the-art methods.