Neural Hand Reconstruction Using A Single RGB Image

We present a neural hand reconstruction method for monocular 3D hand pose and shape estimation in this paper. Instead of directly representing hand with 3D data, a novel UV position map is introduced to represent hand pose and shape with 2D data, which maps 3D hand surface points to 2D image space....
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Gespeichert in:

Autor*in:	Mengcheng Li [verfasserIn] Liang An [verfasserIn] Tao Yu [verfasserIn] Yangang Wang [verfasserIn] Feng Chen [verfasserIn] Yebin Liu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	hand reconstruction CNN single image motion capture

Übergeordnetes Werk:	In: Virtual Reality & Intelligent Hardware - KeAi Communications Co., Ltd., 2020, 2(2020), 3, Seite 276-289
Übergeordnetes Werk:	volume:2 ; year:2020 ; number:3 ; pages:276-289

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.vrih.2020.05.001

Katalog-ID:	DOAJ012672092

Internformat


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520			\|a We present a neural hand reconstruction method for monocular 3D hand pose and shape estimation in this paper. Instead of directly representing hand with 3D data, a novel UV position map is introduced to represent hand pose and shape with 2D data, which maps 3D hand surface points to 2D image space. Furthermore, an encoder-decoder neural network is proposed to infer such UV position map from only single image. To train such network with the lack of ground truth training pairs, we propose a novel MANOReg module which employs MANO model as shape prior to constrain high-dimensional space of UV position map. Both quantitative and qualitative experiments demonstrate the effectiveness of our UV position map representation and MANOReg module.
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spelling	10.1016/j.vrih.2020.05.001 doi (DE-627)DOAJ012672092 (DE-599)DOAJ6714998cd21f458996bf9ca58fad890a DE-627 ger DE-627 rakwb eng TK7885-7895 Mengcheng Li verfasserin aut Neural Hand Reconstruction Using A Single RGB Image 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We present a neural hand reconstruction method for monocular 3D hand pose and shape estimation in this paper. Instead of directly representing hand with 3D data, a novel UV position map is introduced to represent hand pose and shape with 2D data, which maps 3D hand surface points to 2D image space. Furthermore, an encoder-decoder neural network is proposed to infer such UV position map from only single image. To train such network with the lack of ground truth training pairs, we propose a novel MANOReg module which employs MANO model as shape prior to constrain high-dimensional space of UV position map. Both quantitative and qualitative experiments demonstrate the effectiveness of our UV position map representation and MANOReg module. hand reconstruction CNN single image motion capture Computer engineering. Computer hardware Liang An verfasserin aut Tao Yu verfasserin aut Yangang Wang verfasserin aut Feng Chen verfasserin aut Yebin Liu verfasserin aut In Virtual Reality & Intelligent Hardware KeAi Communications Co., Ltd., 2020 2(2020), 3, Seite 276-289 (DE-627)1692190121 (DE-600)3011166-3 26661209 nnns volume:2 year:2020 number:3 pages:276-289 https://doi.org/10.1016/j.vrih.2020.05.001 kostenfrei https://doaj.org/article/6714998cd21f458996bf9ca58fad890a kostenfrei http://www.sciencedirect.com/science/article/pii/S2096579620300371 kostenfrei https://doaj.org/toc/2096-5796 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2020 3 276-289
allfields_unstemmed	10.1016/j.vrih.2020.05.001 doi (DE-627)DOAJ012672092 (DE-599)DOAJ6714998cd21f458996bf9ca58fad890a DE-627 ger DE-627 rakwb eng TK7885-7895 Mengcheng Li verfasserin aut Neural Hand Reconstruction Using A Single RGB Image 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We present a neural hand reconstruction method for monocular 3D hand pose and shape estimation in this paper. Instead of directly representing hand with 3D data, a novel UV position map is introduced to represent hand pose and shape with 2D data, which maps 3D hand surface points to 2D image space. Furthermore, an encoder-decoder neural network is proposed to infer such UV position map from only single image. To train such network with the lack of ground truth training pairs, we propose a novel MANOReg module which employs MANO model as shape prior to constrain high-dimensional space of UV position map. Both quantitative and qualitative experiments demonstrate the effectiveness of our UV position map representation and MANOReg module. hand reconstruction CNN single image motion capture Computer engineering. Computer hardware Liang An verfasserin aut Tao Yu verfasserin aut Yangang Wang verfasserin aut Feng Chen verfasserin aut Yebin Liu verfasserin aut In Virtual Reality & Intelligent Hardware KeAi Communications Co., Ltd., 2020 2(2020), 3, Seite 276-289 (DE-627)1692190121 (DE-600)3011166-3 26661209 nnns volume:2 year:2020 number:3 pages:276-289 https://doi.org/10.1016/j.vrih.2020.05.001 kostenfrei https://doaj.org/article/6714998cd21f458996bf9ca58fad890a kostenfrei http://www.sciencedirect.com/science/article/pii/S2096579620300371 kostenfrei https://doaj.org/toc/2096-5796 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2020 3 276-289
allfieldsGer	10.1016/j.vrih.2020.05.001 doi (DE-627)DOAJ012672092 (DE-599)DOAJ6714998cd21f458996bf9ca58fad890a DE-627 ger DE-627 rakwb eng TK7885-7895 Mengcheng Li verfasserin aut Neural Hand Reconstruction Using A Single RGB Image 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We present a neural hand reconstruction method for monocular 3D hand pose and shape estimation in this paper. Instead of directly representing hand with 3D data, a novel UV position map is introduced to represent hand pose and shape with 2D data, which maps 3D hand surface points to 2D image space. Furthermore, an encoder-decoder neural network is proposed to infer such UV position map from only single image. To train such network with the lack of ground truth training pairs, we propose a novel MANOReg module which employs MANO model as shape prior to constrain high-dimensional space of UV position map. Both quantitative and qualitative experiments demonstrate the effectiveness of our UV position map representation and MANOReg module. hand reconstruction CNN single image motion capture Computer engineering. Computer hardware Liang An verfasserin aut Tao Yu verfasserin aut Yangang Wang verfasserin aut Feng Chen verfasserin aut Yebin Liu verfasserin aut In Virtual Reality & Intelligent Hardware KeAi Communications Co., Ltd., 2020 2(2020), 3, Seite 276-289 (DE-627)1692190121 (DE-600)3011166-3 26661209 nnns volume:2 year:2020 number:3 pages:276-289 https://doi.org/10.1016/j.vrih.2020.05.001 kostenfrei https://doaj.org/article/6714998cd21f458996bf9ca58fad890a kostenfrei http://www.sciencedirect.com/science/article/pii/S2096579620300371 kostenfrei https://doaj.org/toc/2096-5796 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2020 3 276-289
allfieldsSound	10.1016/j.vrih.2020.05.001 doi (DE-627)DOAJ012672092 (DE-599)DOAJ6714998cd21f458996bf9ca58fad890a DE-627 ger DE-627 rakwb eng TK7885-7895 Mengcheng Li verfasserin aut Neural Hand Reconstruction Using A Single RGB Image 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We present a neural hand reconstruction method for monocular 3D hand pose and shape estimation in this paper. Instead of directly representing hand with 3D data, a novel UV position map is introduced to represent hand pose and shape with 2D data, which maps 3D hand surface points to 2D image space. Furthermore, an encoder-decoder neural network is proposed to infer such UV position map from only single image. To train such network with the lack of ground truth training pairs, we propose a novel MANOReg module which employs MANO model as shape prior to constrain high-dimensional space of UV position map. Both quantitative and qualitative experiments demonstrate the effectiveness of our UV position map representation and MANOReg module. hand reconstruction CNN single image motion capture Computer engineering. Computer hardware Liang An verfasserin aut Tao Yu verfasserin aut Yangang Wang verfasserin aut Feng Chen verfasserin aut Yebin Liu verfasserin aut In Virtual Reality & Intelligent Hardware KeAi Communications Co., Ltd., 2020 2(2020), 3, Seite 276-289 (DE-627)1692190121 (DE-600)3011166-3 26661209 nnns volume:2 year:2020 number:3 pages:276-289 https://doi.org/10.1016/j.vrih.2020.05.001 kostenfrei https://doaj.org/article/6714998cd21f458996bf9ca58fad890a kostenfrei http://www.sciencedirect.com/science/article/pii/S2096579620300371 kostenfrei https://doaj.org/toc/2096-5796 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2020 3 276-289
language	English
source	In Virtual Reality & Intelligent Hardware 2(2020), 3, Seite 276-289 volume:2 year:2020 number:3 pages:276-289
sourceStr	In Virtual Reality & Intelligent Hardware 2(2020), 3, Seite 276-289 volume:2 year:2020 number:3 pages:276-289
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	hand reconstruction CNN single image motion capture Computer engineering. Computer hardware
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container_title	Virtual Reality & Intelligent Hardware
authorswithroles_txt_mv	Mengcheng Li @@aut@@ Liang An @@aut@@ Tao Yu @@aut@@ Yangang Wang @@aut@@ Feng Chen @@aut@@ Yebin Liu @@aut@@
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callnumber-first	T - Technology
author	Mengcheng Li
spellingShingle	Mengcheng Li misc TK7885-7895 misc hand reconstruction misc CNN misc single image misc motion capture misc Computer engineering. Computer hardware Neural Hand Reconstruction Using A Single RGB Image
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topic_title	TK7885-7895 Neural Hand Reconstruction Using A Single RGB Image hand reconstruction CNN single image motion capture
topic	misc TK7885-7895 misc hand reconstruction misc CNN misc single image misc motion capture misc Computer engineering. Computer hardware
topic_unstemmed	misc TK7885-7895 misc hand reconstruction misc CNN misc single image misc motion capture misc Computer engineering. Computer hardware
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title	Neural Hand Reconstruction Using A Single RGB Image
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title_full	Neural Hand Reconstruction Using A Single RGB Image
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author_browse	Mengcheng Li Liang An Tao Yu Yangang Wang Feng Chen Yebin Liu
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title_sort	neural hand reconstruction using a single rgb image
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title_auth	Neural Hand Reconstruction Using A Single RGB Image
abstract	We present a neural hand reconstruction method for monocular 3D hand pose and shape estimation in this paper. Instead of directly representing hand with 3D data, a novel UV position map is introduced to represent hand pose and shape with 2D data, which maps 3D hand surface points to 2D image space. Furthermore, an encoder-decoder neural network is proposed to infer such UV position map from only single image. To train such network with the lack of ground truth training pairs, we propose a novel MANOReg module which employs MANO model as shape prior to constrain high-dimensional space of UV position map. Both quantitative and qualitative experiments demonstrate the effectiveness of our UV position map representation and MANOReg module.
abstractGer	We present a neural hand reconstruction method for monocular 3D hand pose and shape estimation in this paper. Instead of directly representing hand with 3D data, a novel UV position map is introduced to represent hand pose and shape with 2D data, which maps 3D hand surface points to 2D image space. Furthermore, an encoder-decoder neural network is proposed to infer such UV position map from only single image. To train such network with the lack of ground truth training pairs, we propose a novel MANOReg module which employs MANO model as shape prior to constrain high-dimensional space of UV position map. Both quantitative and qualitative experiments demonstrate the effectiveness of our UV position map representation and MANOReg module.
abstract_unstemmed	We present a neural hand reconstruction method for monocular 3D hand pose and shape estimation in this paper. Instead of directly representing hand with 3D data, a novel UV position map is introduced to represent hand pose and shape with 2D data, which maps 3D hand surface points to 2D image space. Furthermore, an encoder-decoder neural network is proposed to infer such UV position map from only single image. To train such network with the lack of ground truth training pairs, we propose a novel MANOReg module which employs MANO model as shape prior to constrain high-dimensional space of UV position map. Both quantitative and qualitative experiments demonstrate the effectiveness of our UV position map representation and MANOReg module.
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title_short	Neural Hand Reconstruction Using A Single RGB Image
url	https://doi.org/10.1016/j.vrih.2020.05.001 https://doaj.org/article/6714998cd21f458996bf9ca58fad890a http://www.sciencedirect.com/science/article/pii/S2096579620300371 https://doaj.org/toc/2096-5796
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up_date	2024-07-03T13:24:30.135Z
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