  We propose BAPose, a novel bottom-up approach that achieves state-of-the-art
results for multi-person pose estimation. Our end-to-end trainable framework
leverages a disentangled multi-scale waterfall architecture and incorporates
adaptive convolutions to infer keypoints more precisely in crowded scenes with
occlusions. The multi-scale representations, obtained by the disentangled
waterfall module in BAPose, 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 the challenging
COCO and CrowdPose datasets demonstrate that BAPose is an efficient and robust
framework for multi-person pose estimation, achieving significant improvements
on state-of-the-art accuracy.
