|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM288451325 |
| 003 |
DE-627 |
| 005 |
20231225060333.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231225s2018 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TVCG.2018.2868527
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n0961.xml
|
| 035 |
|
|
|a (DE-627)NLM288451325
|
| 035 |
|
|
|a (NLM)30207957
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Cha, Young-Woon
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Towards Fully Mobile 3D Face, Body, and Environment Capture Using Only Head-worn Cameras
|
| 264 |
|
1 |
|c 2018
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Completed 17.09.2019
|
| 500 |
|
|
|a Date Revised 10.12.2019
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status MEDLINE
|
| 520 |
|
|
|a We propose a new approach for 3D reconstruction of dynamic indoor and outdoor scenes in everyday environments, leveraging only cameras worn by a user. This approach allows 3D reconstruction of experiences at any location and virtual tours from anywhere. The key innovation of the proposed ego-centric reconstruction system is to capture the wearer's body pose and facial expression from near-body views, e.g. cameras on the user's glasses, and to capture the surrounding environment using outward-facing views. The main challenge of the ego-centric reconstruction, however, is the poor coverage of the near-body views - that is, the user's body and face are observed from vantage points that are convenient for wear but inconvenient for capture. To overcome these challenges, we propose a parametric-model-based approach to user motion estimation. This approach utilizes convolutional neural networks (CNNs) for near-view body pose estimation, and we introduce a CNN-based approach for facial expression estimation that combines audio and video. For each time-point during capture, the intermediate model-based reconstructions from these systems are used to re-target a high-fidelity pre-scanned model of the user. We demonstrate that the proposed self-sufficient, head-worn capture system is capable of reconstructing the wearer's movements and their surrounding environment in both indoor and outdoor situations without any additional views. As a proof of concept, we show how the resulting 3D-plus-time reconstruction can be immersively experienced within a virtual reality system (e.g., the HTC Vive). We expect that the size of the proposed egocentric capture-and-reconstruction system will eventually be reduced to fit within future AR glasses, and will be widely useful for immersive 3D telepresence, virtual tours, and general use-anywhere 3D content creation
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a Research Support, Non-U.S. Gov't
|
| 650 |
|
4 |
|a Research Support, U.S. Gov't, Non-P.H.S.
|
| 700 |
1 |
|
|a Price, True
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Wei, Zhen
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Lu, Xinran
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Rewkowski, Nicholas
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Chabra, Rohan
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Qin, Zihe
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Kim, Hyounghun
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Su, Zhaoqi
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Liu, Yebin
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Ilie, Adrian
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a State, Andrei
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Xu, Zhenlin
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Frahm, Jan-Michael
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Fuchs, Henry
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 24(2018), 11 vom: 10. Nov., Seite 2993-3004
|w (DE-627)NLM098269445
|x 1941-0506
|7 nnns
|
| 773 |
1 |
8 |
|g volume:24
|g year:2018
|g number:11
|g day:10
|g month:11
|g pages:2993-3004
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TVCG.2018.2868527
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 24
|j 2018
|e 11
|b 10
|c 11
|h 2993-3004
|