BAPose: Bottom-Up Pose Estimation with Disentangled Waterfall Representations

• URL: http://arxiv.org/abs/2112.10716v1
• Date: Mon, 20 Dec 2021 18:07:09 GMT
• Title: BAPose: Bottom-Up Pose Estimation with Disentangled Waterfall Representations
• Authors: Bruno Artacho, Andreas Savakis
• Abstract summary: BAPose is a novel framework that achieves state-of-the-art results for multi-person pose estimation. Our results on the challenging COCO and CrowdPose datasets demonstrate that BAPose is an efficient and robust framework.
• Score: 3.8073142980733
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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