The DOI matches its Crossref record.
Linear object classes and image synthesis from a single example image
Open accessAbstract
The need to generate new views of a 3D object from a single real image arises in several fields, including graphics and object recognition. While the traditional approach relies on the use of 3D models, simpler techniques are applicable under restricted conditions. The approach exploits image transformations that are specific to the relevant object class, and learnable from example views of other "prototypical" objects of the same class. In this paper, we introduce such a technique by extending the notion of linear class proposed by the authors (1992). For linear object classes, it is shown that linear transformations can be learned exactly from a basis set of 2D prototypical views. We demonstrate the approach on artificial objects and then show preliminary evidence that the technique can effectively "rotate" high-resolution face images from a single 2D view.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
The CAS-PEAL large-scale chinese face database and baseline evaluations
Gao, W., Cao, B., Shan, S., Chen, X., Zhou, D., Zhang, X., & Zhao, D. (2008) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/tsmca.2007.909557 · Cited by 773
Age Synthesis and Estimation via Faces: A Survey
Fu, Y., Guo, G., & Huang, T. S. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.36 · Cited by 569
Measuring facial expressions by computer image analysis
Bartlett, M. S., Hager, J. C., Ekman, P., & Sejnowski, T. J. (1999) · Psychophysiology · doi:10.1017/s0048577299971664 · Cited by 424
An anthropometric face model using variational techniques
DeCarlo, D., Metaxas, D., & Stone, M. (1998) · Proceedings of the 25th annual conference on Computer graphics and interactive techniques - SIGGRAPH '98 · doi:10.1145/280814.280823 · Cited by 327
Efficient, robust and accurate fitting of a 3D morphable model
Romdhani, & Vetter (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238314 · Cited by 189
Metadata
- Authors
- Vetter, T., & Poggio, T.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1997-07
- Volume
- 19
- Issue
- 7
- Pages
- 733-742
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2166125343
- Cited by
- 409
- References
- 0
- FWCI
- 25.75
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
