Deeppose: Human pose estimation via deep neural networks

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 regres-sors 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 formula-tion which capitalizes on recent advances in Deep Learn-ing. We present a detailed empirical analysis with state-of-art or better performance on four academic benchmarks of diverse real-world images. 1