A Hybrid Method for 3D Pose Estimation of Personalized Human Body Models
Ammar Qammaz, Damien Michel, Antonis Argyros · 2018
We propose a new hybrid method for 3D human body pose estimation based on RGBD data. We treat this as an optimization problem that is solved using a stochastic optimization technique. The solution to the optimization problem is the pose parameters of a human model that register it to the available observations. Our method can make use of any skinned, articulated human body model. However, we focus on personalized models that can be acquired easily and automatically based on existing human scanning and mesh rigging techniques. Observations consist of the 3D structure of the human (measured by the RGBD camera) and the body joints locations (computed based on a dis-criminative, CNN-based component). A series of quantitative and qualitative experiments demonstrate the accuracy and the benefits of the proposed approach. In particular, we show that the proposed approach achieves state of the art results compared to competitive methods and that the use of personalized body models improve significantly the accuracy in 3D human pose estimation.