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arXiv · 2110.05132

The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person Pose Estimation

Guillem Brasó·Nikita Kister·Laura Leal-Taixé
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Abstract

We introduce CenterGroup, an attention-based framework to estimate human poses from a set of identity-agnostic keypoints and person center predictions in an image. Our approach uses a transformer to obtain context-aware embeddings for all detected keypoints and centers and then applies multi-head attention to directly group joints into their corresponding person centers. While most bottom-up methods rely on non-learnable clustering at inference, CenterGroup uses a fully differentiable attention mechanism that we train end-to-end together with our keypoint detector. As a result, our method obtains state-of-the-art performance with up to 2.5x faster inference time than competing bottom-up methods. Our code is available at https://github.com/dvl-tum/center-group .

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Guillem Brasó, Nikita Kister, Laura Leal-Taixé. 2021-10-11. The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person Pose Estimation. https://arxiv.org/abs/2110.05132

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