The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
MoFA: Model-Based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction
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Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Cootes, T. F., Edwards, G. J., & Taylor, C. J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.927467 · Cited by 5,409
Robust Face Recognition via Multimodal Deep Face Representation
Ding, C., & Tao, D. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2477042 · Cited by 449
Cited by
RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild
Deng, J., Guo, J., Ververas, E., Kotsia, I., & Zafeiriou, S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00525 · Cited by 1,681
Deepfake Video Detection Using Recurrent Neural Networks
Guera, D., & Delp, E. J. (2018) · 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2018.8639163 · Cited by 1,285
Soft Rasterizer: A Differentiable Renderer for Image-Based 3D Reasoning
Liu, S., Chen, W., Li, T., & Li, H. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00780 · Cited by 641
Learning to Estimate 3D Human Pose and Shape from a Single Color Image
Pavlakos, G., Zhu, L., Zhou, X., & Daniilidis, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00055 · Cited by 607
Generating 3D Faces Using Convolutional Mesh Autoencoders
Ranjan, A., Bolkart, T., Sanyal, S., & Black, M. J. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01219-9_43 · Cited by 560
Metadata
- Authors
- Tewari, A., Zollhofer, M., Kim, H., Garrido, P., Bernard, F., Perez, P., & Theobalt, C.
- Venue
- 2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 1274-1283
- ISSN
- 2473-9936 (ISSN-L)
- OpenAlex
- W2952080583
- Cited by
- 269
- References
- 3
- FWCI
- 8.01
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: MoFA: Model-Based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction. https://app.cochat.ai/papers/10.1109/iccvw.2017.153
- The paper
- Tewari, A., Zollhofer, M., Kim, H., Garrido, P., Bernard, F., Perez, P., & Theobalt, C. (2017). MoFA: Model-Based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction. 2017 IEEE International Conference on Computer Vision Workshops (ICCVW). https://doi.org/10.1109/iccvw.2017.153
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.
