BAPose: Bottom-Up Pose Estimation with Disentangled Waterfall Representations
Bruno Artacho, Andreas E. Savakis · 2023
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 multiscale representations, obtained by the disentangled water-fall module in BAPose, leverage the efficiency of progres-sive filtering in the cascade architecture, while maintaining multi-scale fields-of- view comparable to spatial pyra-mid configurations. Our results on the challenging COCO and CrowdPose datasets demonstrate that BAPose is an efficient and robust framework for multi-person pose estimation, significantly improving state-of-the-art accuracy. Human Pose Estimation, Multi-Scale Representations