Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/626523
Title: Parametric Human Body Reconstruction Based on Sparse Key Points
Authors: Ke-Li Cheng;Ruo-Feng Tong;Min Tang;Jing-Ye Qian;Michel Sarkis
subject: Human body reconstruction|regression|SCAPE modeling|range data
Year: 2016
Publisher: IEEE
Abstract: We propose an automatic parametric human body reconstruction algorithm which can efficiently construct a model using a single Kinect sensor. A user needs to stand still in front of the sensor for a couple of seconds to measure the range data. The user's body shape and pose will then be automatically constructed in several seconds. Traditional methods optimize dense correspondences between range data and meshes. In contrast, our proposed scheme relies on sparse key points for the reconstruction. It employs regression to find the corresponding key points between the scanned range data and some annotated training data. We design two kinds of feature descriptors as well as corresponding regression stages to make the regression robust and accurate. Our scheme follows with dense refinement where a pre-factorization method is applied to improve the computational efficiency. Compared with other methods, our scheme achieves similar reconstruction accuracy but significantly reduces runtime.
Description: 
URI: http://localhost/handle/Hannan/155960
http://localhost/handle/Hannan/626523
ISSN: 1077-2626
volume: 22
issue: 11
Appears in Collections:2016

Files in This Item:
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Title: Parametric Human Body Reconstruction Based on Sparse Key Points
Authors: Ke-Li Cheng;Ruo-Feng Tong;Min Tang;Jing-Ye Qian;Michel Sarkis
subject: Human body reconstruction|regression|SCAPE modeling|range data
Year: 2016
Publisher: IEEE
Abstract: We propose an automatic parametric human body reconstruction algorithm which can efficiently construct a model using a single Kinect sensor. A user needs to stand still in front of the sensor for a couple of seconds to measure the range data. The user's body shape and pose will then be automatically constructed in several seconds. Traditional methods optimize dense correspondences between range data and meshes. In contrast, our proposed scheme relies on sparse key points for the reconstruction. It employs regression to find the corresponding key points between the scanned range data and some annotated training data. We design two kinds of feature descriptors as well as corresponding regression stages to make the regression robust and accurate. Our scheme follows with dense refinement where a pre-factorization method is applied to improve the computational efficiency. Compared with other methods, our scheme achieves similar reconstruction accuracy but significantly reduces runtime.
Description: 
URI: http://localhost/handle/Hannan/155960
http://localhost/handle/Hannan/626523
ISSN: 1077-2626
volume: 22
issue: 11
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7364274.pdf1.53 MBAdobe PDFThumbnail
Preview File
Title: Parametric Human Body Reconstruction Based on Sparse Key Points
Authors: Ke-Li Cheng;Ruo-Feng Tong;Min Tang;Jing-Ye Qian;Michel Sarkis
subject: Human body reconstruction|regression|SCAPE modeling|range data
Year: 2016
Publisher: IEEE
Abstract: We propose an automatic parametric human body reconstruction algorithm which can efficiently construct a model using a single Kinect sensor. A user needs to stand still in front of the sensor for a couple of seconds to measure the range data. The user's body shape and pose will then be automatically constructed in several seconds. Traditional methods optimize dense correspondences between range data and meshes. In contrast, our proposed scheme relies on sparse key points for the reconstruction. It employs regression to find the corresponding key points between the scanned range data and some annotated training data. We design two kinds of feature descriptors as well as corresponding regression stages to make the regression robust and accurate. Our scheme follows with dense refinement where a pre-factorization method is applied to improve the computational efficiency. Compared with other methods, our scheme achieves similar reconstruction accuracy but significantly reduces runtime.
Description: 
URI: http://localhost/handle/Hannan/155960
http://localhost/handle/Hannan/626523
ISSN: 1077-2626
volume: 22
issue: 11
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7364274.pdf1.53 MBAdobe PDFThumbnail
Preview File