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Title: | 3D human pose estimation with simple self-supervised learning | Authors: | Pham, Le Minh Hoang | Keywords: | Deep learning;Squeeze and Excitation Network;SE-net | Issue Date: | 2020 | Publisher: | Trường Đại học Bách khoa - Đại học Đà Nẵng | Abstract: | Recent studies have shown remarkable advances in 3D human pose estimation from monocular images, with the help of large-scale in-door 3D datasets and ophisticated network architectures. However, the generalizability to different environments remains an elusive goal. In this work, we present a solution for single-view 3D human skeleton estimation based on deep learning method. Our network contains two separate model to fully regress and enhance the resulting poses. We utilize a newly proposed model whose name is Squeeze and Excitation Network (SE-net) as to construct our pose estimation network in order to estimate the corresponding pose from a color image; then a model consisting of several blocks of fully-connected networks and a novel semantic graph convolutional networks featuring self-supervision to reconstruct 3D human pose. We demonstrate the effectiveness of our approach on standard datasets for benchmark where we achieved comparable results to some recent state-of-the-art methods. |
Description: | DA.FA.20.027 ; 58 p. |
URI: | http://tainguyenso.dut.udn.vn/handle/DUT/4125 |
Appears in Collections: | DA.Điện tử - Viễn thông |
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7.DA.FA.20.027.PhamLeMinhHoang.pdf | Thuyết minh | 13.82 MB | Adobe PDF | Request a copy |
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