3D Human Pose Estimation from Monocular Images with Deep Convolutional Neural Network

• Published in: Computer Vision -- ACCV 2014 pp. 332 - 347
• Main Authors: Li, Sijin, Chan, Antoni B.
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2015
• Series: Lecture Notes in Computer Science
• Subjects: Convolutional Neural Network; Deep Neural Network; Detection Task; Joint Point; Regression Task
• ISBN: 9783319168074; 331916807X
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-16808-1_23

In this paper, we propose a deep convolutional neural network for 3D human pose estimation from monocular images. We train the network using two strategies: (1) a multi-task framework that jointly trains pose regression and body part detectors; (2) a pre-training strategy where the pose regressor is initialized using a network trained for body part detection. We compare our network on a large data set and achieve significant improvement over baseline methods. Human pose estimation is a structured prediction problem, i.e., the locations of each body part are highly correlated. Although we do not add constraints about the correlations between body parts to the network, we empirically show that the network has disentangled the dependencies among different body parts, and learned their correlations.