  We propose OmniPose, a single-pass, end-to-end trainable framework, that
achieves state-of-the-art results for multi-person pose estimation. Using a
novel waterfall module, the OmniPose architecture leverages multi-scale feature
representations that increase the effectiveness of backbone feature extractors,
without the need for post-processing. OmniPose incorporates contextual
information across scales and joint localization with Gaussian heatmap
modulation at the multi-scale feature extractor to estimate human pose with
state-of-the-art accuracy. The multi-scale representations, obtained by the
improved waterfall module in OmniPose, leverage the efficiency of progressive
filtering in the cascade architecture, while maintaining multi-scale
fields-of-view comparable to spatial pyramid configurations. Our results on
multiple datasets demonstrate that OmniPose, with an improved HRNet backbone
and waterfall module, is a robust and efficient architecture for multi-person
pose estimation that achieves state-of-the-art results.
