Modeling 3D Human Poses from Uncalibrated Monocular Images

This paper introduces an efficient algorithm that reconstructs 3D human poses as well as camera parameters from a small number of 2D point correspondences obtained from uncalibrated monocular images. This problem is challenging because 2D image constraints (e. g. 2D point correspondences) are often not sufficient to determine 3D poses of an articulated object. The key idea of this paper is to identify a set of new constraints and use them to eliminate the ambiguity of 3D pose reconstruction. We also develop an optimization process to simultaneously reconstruct both human poses and camera parameters from various forms of reconstruction constraints. We demonstrate the power and effectiveness of our system by evaluating the performance of the algorithm on both real and synthetic data. We show the algorithm can accurately reconstruct 3D poses and camera parameters from a wide variety of real images, including internet photos and key frames extracted from monocular video sequences.