Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 15th IAPR International Conference on Machine Vision Applications |
| Subtitle of host publication | MVA 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 298-301 |
| Number of pages | 4 |
| ISBN (electronic) | 9784901122160 |
| Publication status | Published - 19 Jul 2017 |
| Event | 15th IAPR International Conference on Machine Vision Applications, MVA 2017 - Nagoya, Japan Duration: 8 May 2017 → 12 May 2017 |
Abstract
Nonrigid Structure-From-Motion is a well-known approach to estimate time-varying 3D structures from 2D input image sequences. For challenging problems such as the reconstruction of human faces, state-of-the-art approaches estimate statistical shape spaces from training data. It is common practice to use orthographic or weak-perspective camera models to map 3D to 2D points. We propose to use a projective camera model combined with a multilinear tensor-based face model, enabling approximation of a dense 3D face surface by sparse 2D landmarks. Using a projective camera is beneficial, as it is able to handle perspective projections and particular camera motions which are critical for affine models. We show how the nonlinearity of the projective model can be linearized so that its parameters can be estimated by an alternating-least-squares approach. This enables simple and fast estimation of the model parameters. The effectiveness of the proposed algorithm is demonstrated using challenging real image data.
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Computer Vision and Pattern Recognition
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Proceedings of the 15th IAPR International Conference on Machine Vision Applications: MVA 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 298-301 7986860.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Projective Structure from Facial Motion
AU - Grashof, Stella
AU - Ackermann, Hanno
AU - Kuhnke, Felix
AU - Ostermann, Jorn
AU - Brandt, Sami S.
N1 - Funding information: This work was partly supported by German Research Foundation grant DFG AC 264-2/1.
PY - 2017/7/19
Y1 - 2017/7/19
N2 - Nonrigid Structure-From-Motion is a well-known approach to estimate time-varying 3D structures from 2D input image sequences. For challenging problems such as the reconstruction of human faces, state-of-the-art approaches estimate statistical shape spaces from training data. It is common practice to use orthographic or weak-perspective camera models to map 3D to 2D points. We propose to use a projective camera model combined with a multilinear tensor-based face model, enabling approximation of a dense 3D face surface by sparse 2D landmarks. Using a projective camera is beneficial, as it is able to handle perspective projections and particular camera motions which are critical for affine models. We show how the nonlinearity of the projective model can be linearized so that its parameters can be estimated by an alternating-least-squares approach. This enables simple and fast estimation of the model parameters. The effectiveness of the proposed algorithm is demonstrated using challenging real image data.
AB - Nonrigid Structure-From-Motion is a well-known approach to estimate time-varying 3D structures from 2D input image sequences. For challenging problems such as the reconstruction of human faces, state-of-the-art approaches estimate statistical shape spaces from training data. It is common practice to use orthographic or weak-perspective camera models to map 3D to 2D points. We propose to use a projective camera model combined with a multilinear tensor-based face model, enabling approximation of a dense 3D face surface by sparse 2D landmarks. Using a projective camera is beneficial, as it is able to handle perspective projections and particular camera motions which are critical for affine models. We show how the nonlinearity of the projective model can be linearized so that its parameters can be estimated by an alternating-least-squares approach. This enables simple and fast estimation of the model parameters. The effectiveness of the proposed algorithm is demonstrated using challenging real image data.
UR - http://www.scopus.com/inward/record.url?scp=85027883345&partnerID=8YFLogxK
U2 - 10.23919/mva.2017.7986860
DO - 10.23919/mva.2017.7986860
M3 - Conference contribution
AN - SCOPUS:85027883345
SP - 298
EP - 301
BT - Proceedings of the 15th IAPR International Conference on Machine Vision Applications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IAPR International Conference on Machine Vision Applications, MVA 2017
Y2 - 8 May 2017 through 12 May 2017
ER -