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Uncalibrated Non-Rigid Factorisation by Independent Subspace Analysis

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Standard

Uncalibrated Non-Rigid Factorisation by Independent Subspace Analysis. / Brandt, Sami Sebastian; Ackermann, Hanno; Grasshof, Stella.

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). IEEE, 2019.

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

Harvard

Brandt, SS, Ackermann, H & Grasshof, S 2019, Uncalibrated Non-Rigid Factorisation by Independent Subspace Analysis. in 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). IEEE. https://doi.org/10.1109/ICCVW.2019.00070

APA

Brandt, S. S., Ackermann, H., & Grasshof, S. (2019). Uncalibrated Non-Rigid Factorisation by Independent Subspace Analysis. In 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) IEEE. https://doi.org/10.1109/ICCVW.2019.00070

Vancouver

Brandt SS, Ackermann H, Grasshof S. Uncalibrated Non-Rigid Factorisation by Independent Subspace Analysis. In 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). IEEE. 2019 https://doi.org/10.1109/ICCVW.2019.00070

Author

Brandt, Sami Sebastian ; Ackermann, Hanno ; Grasshof, Stella. / Uncalibrated Non-Rigid Factorisation by Independent Subspace Analysis. 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). IEEE, 2019.

Bibtex

@inproceedings{076bc7b564f8475c8b8e97efd82c09e1,
title = "Uncalibrated Non-Rigid Factorisation by Independent Subspace Analysis",
abstract = "We propose a general, prior-free approach for the uncalibrated non-rigid structure-from-motion problem for modelling and analysis of non-rigid objects such as human faces. We recover the non-rigid affine structure and motion from 2D point correspondences by assuming that (1) the non-rigid shapes are generated by a linear combination of rigid 3D basis shapes, (2) that the non-rigid shapes are affine in nature, i.e., they can be modelled as deviations from the mean, rigid shape, (3) and that the basis shapes are statistically independent. In contrast to the majority of existing works, no statistical prior is assumed for the structure and motion apart from the assumption the that underlying basis shapes are statistically independent. The independent 3D shape bases are recovered by independent subspace analysis (ISA). Likewise, in contrast to the most previous approaches, no calibration information is assumed for affine cameras; the reconstruction is solved up to a global affine ambiguity that makes our approach simple and efficient. In the experiments, we evaluated the method with several standard data sets including a real face expression data set of 7200 faces with 2D point correspondences and unknown 3D structure and motion for which we obtained promising results.",
author = "Brandt, {Sami Sebastian} and Hanno Ackermann and Stella Grasshof",
year = "2019",
month = oct,
day = "27",
doi = "10.1109/ICCVW.2019.00070",
language = "English",
booktitle = "2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)",
publisher = "IEEE",
address = "United States",

}

RIS

TY - GEN

T1 - Uncalibrated Non-Rigid Factorisation by Independent Subspace Analysis

AU - Brandt, Sami Sebastian

AU - Ackermann, Hanno

AU - Grasshof, Stella

PY - 2019/10/27

Y1 - 2019/10/27

N2 - We propose a general, prior-free approach for the uncalibrated non-rigid structure-from-motion problem for modelling and analysis of non-rigid objects such as human faces. We recover the non-rigid affine structure and motion from 2D point correspondences by assuming that (1) the non-rigid shapes are generated by a linear combination of rigid 3D basis shapes, (2) that the non-rigid shapes are affine in nature, i.e., they can be modelled as deviations from the mean, rigid shape, (3) and that the basis shapes are statistically independent. In contrast to the majority of existing works, no statistical prior is assumed for the structure and motion apart from the assumption the that underlying basis shapes are statistically independent. The independent 3D shape bases are recovered by independent subspace analysis (ISA). Likewise, in contrast to the most previous approaches, no calibration information is assumed for affine cameras; the reconstruction is solved up to a global affine ambiguity that makes our approach simple and efficient. In the experiments, we evaluated the method with several standard data sets including a real face expression data set of 7200 faces with 2D point correspondences and unknown 3D structure and motion for which we obtained promising results.

AB - We propose a general, prior-free approach for the uncalibrated non-rigid structure-from-motion problem for modelling and analysis of non-rigid objects such as human faces. We recover the non-rigid affine structure and motion from 2D point correspondences by assuming that (1) the non-rigid shapes are generated by a linear combination of rigid 3D basis shapes, (2) that the non-rigid shapes are affine in nature, i.e., they can be modelled as deviations from the mean, rigid shape, (3) and that the basis shapes are statistically independent. In contrast to the majority of existing works, no statistical prior is assumed for the structure and motion apart from the assumption the that underlying basis shapes are statistically independent. The independent 3D shape bases are recovered by independent subspace analysis (ISA). Likewise, in contrast to the most previous approaches, no calibration information is assumed for affine cameras; the reconstruction is solved up to a global affine ambiguity that makes our approach simple and efficient. In the experiments, we evaluated the method with several standard data sets including a real face expression data set of 7200 faces with 2D point correspondences and unknown 3D structure and motion for which we obtained promising results.

U2 - 10.1109/ICCVW.2019.00070

DO - 10.1109/ICCVW.2019.00070

M3 - Article in proceedings

BT - 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

PB - IEEE

ER -

ID: 84773230