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2020 25th International Conference on Pattern Recognition (ICPR)
Human Pose Estimation is a fundamental task for many applications in the Computer Vision community and it has been widely investigated in the 2D domain, i.e. intensity images. Therefore, most of the available methods for this task are mainly based on 2D Convolutional Neural Networks and huge manuallyannotated RGB datasets, achieving stunning results. In this paper, we propose RefiNet, a multi-stage framework that regresses an extremely-precise 3D human pose estimation from a given 2D pose and adoi:10.1109/icpr48806.2021.9412451 fatcat:ymjvqhzym5cd3bwkfasjcuebvu