Exploiting Human Bipedal Motion Constraints for 3D Pose Recovery from a Single Uncalibrated Camera

A new method is proposed for recovering 3D human poses in video sequences taken from a single uncalibrated camera. This is achieved by exploiting two important constraints observed from human bipedal motion: coplanarity of body key points during the mid-stance position and the presence of a foot on the ground – i.e. static foot during most activities. Assuming 2D joint locations have been extracted from a video sequence, the algorithm is able to perform camera auto-calibration on specific frames when the human body adopts particular postures. Then, a simplified pin-hole camera model is used to perform 3D pose reconstruction on the calibrated frames. Finally, the static foot constraint which is found in most human bipedal motions is applied to infer body postures for non-calibrated frames. We compared our method with (1) “orthographic reconstruction” method and (2) reconstruction using manually calibrated data. The results validate the assumptions made for the simplified pin-hole camera model and reconstruction results reveal a significant improvement over the orthographic reconstruction method.