This dissertation deals with the problem of capturing human motions and poses using a single camera. The frst part of the thesis proposes two closely related approaches for the 3D reconstruction of human motions from image sequences. To resolve inherent ambiguities in monocular 3D reconstruction the main idea of this part is to exploit temporal properties of human motions in combination with a human body model learned from training data. The second part of the thesis tackles the problem of reconstructing a human pose from a single image. A human body model is learned by training a deep neural network that covers nonlinearities and anthropometric constraints.
C O N T E N T S
1 Introduction ….. 1
1.1 Applications and Commercial Systems . . . . . . . . . . . 1
