3D Head Pose Estimation Using Non-rigid Structure-from-motion and Point Correspondence

Citations Over Time

Abstract

This paper addresses tracking and 3D pose estimation of human faces with large pose and expression changes in video sequences obtained from an un-calibrated monocular camera. The classical pose estimation methods suffer from two disadvantages: (1) a 3D head model or a reference frame is always needed and the camera should be calibrated in advance; (2) it is difficult to deal with non-rigid motion, which is very common for human faces. In this paper, we present a pose estimation system, which is able to overcome the above disadvantages. For each frame, a 2D active appearance model is adopted to reliably track the face and facial features with large pose and expression variations. Then we utilize a recently developed non-rigid structure from motion (SFM) technique to recover the 3D face shape. Instead of direct using the rotation matrix resulted from SFM, we propose a method to use robust statistics and 3D-2D feature point correspondence to accurately recover the 3D head pose. Our experiments have demonstrated the effectiveness and efficiency of the approach.

Related Papers

• → Learning to Refine Human Pose Estimation(2018)112 cited
• → Shape-Constraint Recurrent Flow for 6D Object Pose Estimation(2023)27 cited
• → Learning to Refine Human Pose Estimation(2018)9 cited
• A Comparison on Different 2D Human Pose Estimation Method(2018)