. Besides extreme variability in articulations, many of the joints are barely visible. We can guess the location of the right arm in the left image only because we see the rest of the pose and anticipate the motion or activity of the person. Similarly, the left body half of the person on the right is not visible at all. These are examples of the need for holistic reasoning. We believe that DNNs can naturally provide such type of reasoning. We propose a method for human pose estimation based on Deep Neural Networks (DNNs). The pose estimation is formulated as a DNN-based regression problem towards body joints. We present a cascade of such DNN regressors which results in high precision pose estimates. The approach has the advantage of reasoning about pose in a holistic fashion and has a simple but yet powerful formulation which capitalizes on recent advances in Deep Learning. We present a detailed empirical analysis with state-ofart or better performance on four academic benchmarks of diverse real-world images.
Toshev and Szegedy REF is the first one to introduce deep neural network for human pose estimation and formulate the pose estimation as a joint regression problem.
DeepPose: Human Pose Estimation via Deep Neural Networks
ArXiv
ArXiv
Computer Science