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[bibtex]@InProceedings{Braso_2021_ICCV, author = {Bras\'o, Guillem and Kister, Nikita and Leal-Taix\'e, Laura}, title = {The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person Pose Estimation}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {11853-11863} }
The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person Pose Estimation
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.
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