  We propose UniPose, a unified framework for human pose estimation, based on
our "Waterfall" Atrous Spatial Pooling architecture, that achieves
state-of-art-results on several pose estimation metrics. Current pose
estimation methods utilizing standard CNN architectures heavily rely on
statistical postprocessing or predefined anchor poses for joint localization.
UniPose incorporates contextual segmentation and joint localization to estimate
the human pose in a single stage, with high accuracy, without relying on
statistical postprocessing methods. The Waterfall module in UniPose leverages
the efficiency of progressive filtering in the cascade architecture, while
maintaining multi-scale fields-of-view comparable to spatial pyramid
configurations. Additionally, our method is extended to UniPose-LSTM for
multi-frame processing and achieves state-of-the-art results for temporal pose
estimation in Video. Our results on multiple datasets demonstrate that UniPose,
with a ResNet backbone and Waterfall module, is a robust and efficient
architecture for pose estimation obtaining state-of-the-art results in single
person pose detection for both single images and videos.
