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Automatic 3D face reconstruction from single images or video

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Published

Standard

Automatic 3D face reconstruction from single images or video. / Breuer, Pia; Kim, Kwang In; Kienzle, Wolf; Schölkopf, Bernhard; Blanz, Volker.

Automatic Face & Gesture Recognition, 2008. FG '08. 8th IEEE International Conference on. IEEE, 2008. p. 1-8.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

Breuer, P, Kim, KI, Kienzle, W, Schölkopf, B & Blanz, V 2008, Automatic 3D face reconstruction from single images or video. in Automatic Face & Gesture Recognition, 2008. FG '08. 8th IEEE International Conference on. IEEE, pp. 1-8. https://doi.org/10.1109/AFGR.2008.4813339

APA

Breuer, P., Kim, K. I., Kienzle, W., Schölkopf, B., & Blanz, V. (2008). Automatic 3D face reconstruction from single images or video. In Automatic Face & Gesture Recognition, 2008. FG '08. 8th IEEE International Conference on (pp. 1-8). IEEE. https://doi.org/10.1109/AFGR.2008.4813339

Vancouver

Breuer P, Kim KI, Kienzle W, Schölkopf B, Blanz V. Automatic 3D face reconstruction from single images or video. In Automatic Face & Gesture Recognition, 2008. FG '08. 8th IEEE International Conference on. IEEE. 2008. p. 1-8 https://doi.org/10.1109/AFGR.2008.4813339

Author

Breuer, Pia ; Kim, Kwang In ; Kienzle, Wolf ; Schölkopf, Bernhard ; Blanz, Volker. / Automatic 3D face reconstruction from single images or video. Automatic Face & Gesture Recognition, 2008. FG '08. 8th IEEE International Conference on. IEEE, 2008. pp. 1-8

Bibtex

@inproceedings{adffd84a376f4e709845774fafd24cfc,
title = "Automatic 3D face reconstruction from single images or video",
abstract = "This paper presents a fully automated algorithm for reconstructing a textured 3D model of a face from a single photograph or a raw video stream. The algorithm is based on a combination of Support Vector Machines (SVMs) and a Morphable Model of 3D faces. After SVM face detection, individual facial features are detected using a novel regression- and classification-based approach, and probabilistically plausible configurations of features are selected to produce a list of candidates for several facial feature positions. In the next step, the configurations of feature points are evaluated using a novel criterion that is based on a Morphable Model and a combination of linear projections. To make the algorithm robust with respect to head orientation, this process is iterated while the estimate of pose is refined. Finally, the feature points initialize a model-fitting procedure of the Morphable Model. The result is a high resolution 3D surface model.",
author = "Pia Breuer and Kim, {Kwang In} and Wolf Kienzle and Bernhard Sch{\"o}lkopf and Volker Blanz",
year = "2008",
doi = "10.1109/AFGR.2008.4813339",
language = "English",
isbn = "9781424421534",
pages = "1--8",
booktitle = "Automatic Face & Gesture Recognition, 2008. FG '08. 8th IEEE International Conference on",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Automatic 3D face reconstruction from single images or video

AU - Breuer, Pia

AU - Kim, Kwang In

AU - Kienzle, Wolf

AU - Schölkopf, Bernhard

AU - Blanz, Volker

PY - 2008

Y1 - 2008

N2 - This paper presents a fully automated algorithm for reconstructing a textured 3D model of a face from a single photograph or a raw video stream. The algorithm is based on a combination of Support Vector Machines (SVMs) and a Morphable Model of 3D faces. After SVM face detection, individual facial features are detected using a novel regression- and classification-based approach, and probabilistically plausible configurations of features are selected to produce a list of candidates for several facial feature positions. In the next step, the configurations of feature points are evaluated using a novel criterion that is based on a Morphable Model and a combination of linear projections. To make the algorithm robust with respect to head orientation, this process is iterated while the estimate of pose is refined. Finally, the feature points initialize a model-fitting procedure of the Morphable Model. The result is a high resolution 3D surface model.

AB - This paper presents a fully automated algorithm for reconstructing a textured 3D model of a face from a single photograph or a raw video stream. The algorithm is based on a combination of Support Vector Machines (SVMs) and a Morphable Model of 3D faces. After SVM face detection, individual facial features are detected using a novel regression- and classification-based approach, and probabilistically plausible configurations of features are selected to produce a list of candidates for several facial feature positions. In the next step, the configurations of feature points are evaluated using a novel criterion that is based on a Morphable Model and a combination of linear projections. To make the algorithm robust with respect to head orientation, this process is iterated while the estimate of pose is refined. Finally, the feature points initialize a model-fitting procedure of the Morphable Model. The result is a high resolution 3D surface model.

U2 - 10.1109/AFGR.2008.4813339

DO - 10.1109/AFGR.2008.4813339

M3 - Conference contribution/Paper

SN - 9781424421534

SP - 1

EP - 8

BT - Automatic Face & Gesture Recognition, 2008. FG '08. 8th IEEE International Conference on

PB - IEEE

ER -