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.
