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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2023.3307338
|2 doi
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|a pubmed24n1203.xml
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|a (DE-627)NLM361056974
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|a (NLM)37607140
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Zhu, Hao
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a FaceScape
|b 3D Facial Dataset and Benchmark for Single-View 3D Face Reconstruction
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|c 2023
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 06.11.2023
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|a Date Revised 13.11.2023
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a In this article, we present a large-scale detailed 3D face dataset, FaceScape, and the corresponding benchmark to evaluate single-view facial 3D reconstruction. By training on FaceScape data, a novel algorithm is proposed to predict elaborate riggable 3D face models from a single image input. FaceScape dataset releases 16,940 textured 3D faces, captured from 847 subjects and each with 20 specific expressions. The 3D models contain the pore-level facial geometry that is also processed to be topologically uniform. These fine 3D facial models can be represented as a 3D morphable model for coarse shapes and displacement maps for detailed geometry. Taking advantage of the large-scale and high-accuracy dataset, a novel algorithm is further proposed to learn the expression-specific dynamic details using a deep neural network. The learned relationship serves as the foundation of our 3D face prediction system from a single image input. Different from most previous methods, our predicted 3D models are riggable with highly detailed geometry under different expressions. We also use FaceScape data to generate the in-the-wild and in-the-lab benchmark to evaluate recent methods of single-view face reconstruction. The accuracy is reported and analyzed on the dimensions of camera pose and focal length, which provides a faithful and comprehensive evaluation and reveals new challenges. The unprecedented dataset, benchmark, and code have been released to the public for research purpose
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|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Yang, Haotian
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Guo, Longwei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Yidi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Yanru
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Huang, Mingkai
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wu, Menghua
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Shen, Qiu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yang, Ruigang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Cao, Xun
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 12 vom: 01. Dez., Seite 14528-14545
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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|g volume:45
|g year:2023
|g number:12
|g day:01
|g month:12
|g pages:14528-14545
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|u http://dx.doi.org/10.1109/TPAMI.2023.3307338
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