The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Learning a model of facial shape and expression from 4D scans
Abstract
The field of 3D face modeling has a large gap between high-end and low-end methods. At the high end, the best facial animation is indistinguishable from real humans, but this comes at the cost of extensive manual labor. At the low end, face capture from consumer depth sensors relies on 3D face models that are not expressive enough to capture the variability in natural facial shape and expression. We seek a middle ground by learning a facial model from thousands of accurately aligned 3D scans. Our FLAME model (Faces Learned with an Articulated Model and Expressions) is designed to work with existing graphics software and be easy to fit to data. FLAME uses a linear shape space trained from 3800 scans of human heads. FLAME combines this linear shape space with an articulated jaw, neck, and eyeballs, pose-dependent corrective blendshapes, and additional global expression blendshapes. The pose and expression dependent articulations are learned from 4D face sequences in the D3DFACS dataset along with additional 4D sequences. We accurately register a template mesh to the scan sequences and make the D3DFACS registrations available for research purposes. In total the model is trained from over 33, 000 scans. FLAME is low-dimensional but more expressive than the FaceWarehouse model and the Basel Face Model. We compare FLAME to these models by fitting them to static 3D scans and 4D sequences using the same optimization method. FLAME is significantly more accurate and is available for research purposes (http://flame.is.tue.mpg.de).
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Cites
Scikit-learn: Machine Learning in Python
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Ekman, P., & Friesen, W. V. (2019) · PsycTESTS Dataset · doi:10.1037/t27734-000 · Cited by 6,019
Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & J, B. M. (2015) · ACM Transactions on Graphics · doi:10.1145/2816795.2818013 · Cited by 3,734
Self-citationSCAPE: shape completion and animation of people
Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., & Davis, J. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073207 · Cited by 1,489
A 3D Facial Expression Database For Facial Behavior Research
Yin, L., Wei, X., Sun, Y., Wang, J., & Rosato, M. J. (2006) · 7th International Conference on Automatic Face and Gesture Recognition (FGR06) · doi:10.1109/fgr.2006.6 · Cited by 1,171
The space of human body shapes: reconstruction and parameterization from range scans
Allen, B., Curless, B., & Popović, Z. (2003) · ACM SIGGRAPH 2003 Papers · doi:10.1145/1201775.882311 · Cited by 913
Realtime performance-based facial animation
Weise, T., Bouaziz, S., Li, H., & Pauly, M. (2011) · ACM Transactions on Graphics · doi:10.1145/2010324.1964972 · Cited by 356
Self-citation
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Self-citationAMASS: Archive of Motion Capture As Surface Shapes
Mahmood, N., Ghorbani, N., Troje, N. F., Pons-Moll, G., & Black, M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00554 · Cited by 1,323
Self-citationCapture, Learning, and Synthesis of 3D Speaking Styles
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Self-citationAI-generated characters for supporting personalized learning and well-being
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Neural Rendering and Reenactment of Human Actor Videos
Liu, L., Xu, W., Zollhöfer, M., Kim, H., Bernard, F., Habermann, M., Wang, W., & Theobalt, C. (2019) · ACM Transactions on Graphics · doi:10.1145/3333002 · Cited by 163
Metadata
- Authors
- Li, T., Bolkart, T., Black, M. J., Li, H., & Romero, J.
- Venue
- ACM Transactions on Graphics
- Published
- 2017-11-20
- Volume
- 36
- Issue
- 6
- Pages
- 1-17
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2769666294
- Cited by
- 1,223
- References
- 7
- FWCI
- 14.49
- Type
- journal article
- Language
- en
- Open access
- closed
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- Li, T., Bolkart, T., Black, M. J., Li, H., & Romero, J. (2017). Learning a model of facial shape and expression from 4D scans. ACM Transactions on Graphics. https://doi.org/10.1145/3130800.3130813
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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