Multi-person pose estimation using skeleton prediction

Abstract

Embodiments provide functionality for identifying joints and limbs in images. An embodiment extracts features from an image to generate feature maps and, in turn, processes the feature maps using a single convolutional neural network trained based on a target model that includes joints and limbs. The processing generates both a directionless joint confidence map indicating confidence with which pixels in the image depict one or more joints and a directionless limb confidence map indicating confidence with which the pixels in the image depict one or more limbs between adjacent joints of the one or more joints, wherein adjacency of joints is provided by the target model. To continue, indications of the one or more joints and the one or more limbs in the image are generated using the directionless joint confidence map, the directionless limb confidence map, and the target model. Embodiments can be deployed on mobile and embedded systems.