Poseur: Direct Human Pose Regression with Transformers

• URL: http://arxiv.org/abs/2201.07412v1
• Date: Wed, 19 Jan 2022 04:31:57 GMT
• Title: Poseur: Direct Human Pose Regression with Transformers
• Authors: Weian Mao and Yongtao Ge and Chunhua Shen and Zhi Tian and Xinlong Wang and Zhibin Wang and Anton van den Hengel
• Abstract summary: We propose a direct, regression-based approach to 2D human pose estimation from single images. Our framework is end-to-end differentiable, and naturally learns to exploit the dependencies between keypoints. Ours is the first regression-based approach to perform favorably compared to the best heatmap-based pose estimation methods.
• Score: 119.79232258661995
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We propose a direct, regression-based approach to 2D human pose estimation from single images. We formulate the problem as a sequence prediction task, which we solve using a Transformer network. This network directly learns a regression mapping from images to the keypoint coordinates, without resorting to intermediate representations such as heatmaps. This approach avoids much of the complexity associated with heatmap-based approaches. To overcome the feature misalignment issues of previous regression-based methods, we propose an attention mechanism that adaptively attends to the features that are most relevant to the target keypoints, considerably improving the accuracy. Importantly, our framework is end-to-end differentiable, and naturally learns to exploit the dependencies between keypoints. Experiments on MS-COCO and MPII, two predominant pose-estimation datasets, demonstrate that our method significantly improves upon the state-of-the-art in regression-based pose estimation. More notably, ours is the first regression-based approach to perform favorably compared to the best heatmap-based pose estimation methods.

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