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BAPose: Bottom-Up Pose Estimation with Disentangled Waterfall Representations
[article]
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
arXiv:2112.10716v1
fatcat:oqnfato4qrd5jfjm35wu4ipupi