BAPose: Bottom-Up Pose Estimation with Disentangled Waterfall Representations [article]

Bruno Artacho, Andreas Savakis
2021 arXiv   pre-print
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
more » ... taining 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.
arXiv:2112.10716v1 fatcat:oqnfato4qrd5jfjm35wu4ipupi