PE-former: Pose Estimation Transformer

• URL: http://arxiv.org/abs/2112.04981v1
• Date: Thu, 9 Dec 2021 15:20:23 GMT
• Title: PE-former: Pose Estimation Transformer
• Authors: Paschalis Panteleris, Antonis Argyros
• Abstract summary: We investigate the use of a pure transformer architecture for the problem of 2D body pose estimation. We demonstrate that using an encoder-decoder transformer architecture yields state of the art results on this estimation problem.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision transformer architectures have been demonstrated to work very effectively for image classification tasks. Efforts to solve more challenging vision tasks with transformers rely on convolutional backbones for feature extraction. In this paper we investigate the use of a pure transformer architecture (i.e., one with no CNN backbone) for the problem of 2D body pose estimation. We evaluate two ViT architectures on the COCO dataset. We demonstrate that using an encoder-decoder transformer architecture yields state of the art results on this estimation problem.

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