Towards Fast, Accurate and Stable 3D Dense Face Alignment
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FakeAVCeleb does not provide predefined train/validation/test splits.
“For instance, FakeAVCeleb does not provide predefined train/validation/test splits.”
From Deepfake Detection: Analyzing Model Generalization Across Architectures, Datasets, and Pre-Training Paradigms (2024) · cites this paper as [45]
All four datasets comprise real and fake videos, where fake videos are generated using different deepfake generation methods.
“All of the four datasets comprise of real and fake videos, where fake videos are generated using different deepfake generation methods.”
From Deepfake Detection: Analyzing Model Generalization Across Architectures, Datasets, and Pre-Training Paradigms (2024) · cites this paper as [45]
Cites
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Finn, C., Abbeel, P., & Levine, S. (2017) · arXiv · doi:10.48550/arxiv.1703.03400 · Cited by 8,512 · see citing works
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.220 · Cited by 6,542 · see citing works
Stacked Hourglass Networks for Human Pose Estimation
Newell, A., Yang, K., & Deng, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46484-8_29 · Cited by 5,634 · see citing works
A morphable model for the synthesis of 3D faces
Blanz, V., & Vetter, T. R. (1999) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/311535.311556 · Cited by 4,695 · see citing works
Face detection, pose estimation, and landmark localization in the wild
Zhu, X., & Ramanan, D. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6248014 · Cited by 2,171 · see citing works
Self-citationBulat, A., & Tzimiropoulos, G. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.116 · Cited by 1,464 · see citing works
Bulat, A., & Tzimiropoulos, G. (2017) · Nottingham ePrints (University of Nottingham) · Cited by 1,277 · see citing works
XM2VTSDB: The Extended M2VTS Database
Messer, K., Matas, J., Kittler, J., Luettin, J., & Maître, G. (1999) · Cited by 1,158 · see citing works
Face Alignment Across Large Poses: A 3D Solution
Zhu, X., Lei, Z., Liu, X., Shi, H., & Li, S. Z. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.23 · Cited by 1,137 · see citing works
Self-citation300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge
Sagonas, C., Tzimiropoulos, G., Zafeiriou, S. P., & Pantić, M. (2013) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2013.59 · Cited by 1,123 · see citing works
Face Alignment by Explicit Shape Regression
Cao, X., Wei, Y., Wen, F., & Sun, J. Z. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0667-3 · Cited by 1,038 · see citing works
Köstinger, M., Wohlhart, P., Roth, P. M., & Bischof, H. (2011) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2011.6130513 · Cited by 1,004 · see citing works
Accurate 3D Face Reconstruction With Weakly-Supervised Learning: From Single Image to Image Set
Deng, Y., Yang, J., Xu, S., Chen, D., Jia, Y., & Tong, X. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00038 · Cited by 776 · see citing works
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
Feng, Y., Wu, F., Shao, X., Wang, Y., & Zhou, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01264-9_33 · Cited by 768 · see citing works
Kim, H., Garrido, P., Tewari, A., Xu, W., Thies, J., Nießner, M., Pérez, P., Richardt, C., Zollhöfer, M., & Theobalt, C. (2018) · ACM Transactions on Graphics · doi:10.1145/3197517.3201283 · Cited by 692 · see citing works
Searching Central Difference Convolutional Networks for Face Anti-Spoofing
Yu, Z., Zhao, C., Wang, Z., Qin, Y., Su, Z., Li, X., Zhou, F., & Zhao, G. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00534 · Cited by 672 · see citing works
Monocular Model-Based 3D Tracking of Rigid Objects: A Survey
Lepetit, V., & Fua, P. (2005) · now publishers, Inc. eBooks · doi:10.1561/9781933019536 · Cited by 536 · see citing works
Localizing Parts of Faces Using a Consensus of Exemplars
Belhumeur, P. N., Jacobs, D. W., Kriegman, D. J., & Kumar, N. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.23 · Cited by 522 · see citing works
Regressing Robust and Discriminative 3D Morphable Models with a Very Deep Neural Network
Tran, A. T., Hassner, T., Masi, I., & Medioni, G. G. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.163 · Cited by 503 · see citing works
Lookahead Optimizer: k steps forward, 1 step backPreprint
Zhang, M. R., Lucas, J., Hinton, G., & Ba, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.08610 · Cited by 470 · see citing works
Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression
Jackson, A. S., Bulat, A., Argyriou, V., & Tzimiropoulos, G. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.117 · Cited by 457 · see citing works
MoFA: Model-Based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction
Tewari, A., Zollhöfer, M., Kim, H., Garrido, P., Bernard, F., Pérez, P., & Theobalt, C. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.401 · Cited by 398 · see citing works
Face Alignment in Full Pose Range: A 3D Total Solution
Zhu, X., Liu, X., Lei, Z., & Li, S. Z. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2778152 · Cited by 391 · see citing works
Self-citationExtensive Facial Landmark Localization with Coarse-to-Fine Convolutional Network Cascade
Zhou, E., Fan, H., Cao, Z., et al. (2013) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2013.58 · Cited by 373 · see citing works
A 3D Morphable Model Learnt from 10,000 Faces
Booth, J. G., Roussos, A., Zafeiriou, S. P., Ponniah, A. J. T., & Dunaway, D. J. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.598 · Cited by 352 · see citing works
3D shape regression for real-time facial animation
Cao, C., Weng, Y., Lin, S. C.-F., & Zhou, K. (2013) · ACM Transactions on Graphics · doi:10.1145/2461912.2462012 · Cited by 348 · see citing works
GANFIT: generative adversarial network fitting for high fidelity 3D face reconstruction
Gecer, B., Ploumpis, S., Kotsia, I., & Zafeiriou, S. (2019) · Spiral (Imperial College London) · Cited by 311 · see citing works
GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction
Gecer, B., Ploumpis, S., Kotsia, I., & Zafeiriou, S. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00125 · Cited by 303 · see citing works
The First Facial Landmark Tracking in-the-Wild Challenge: Benchmark and Results
Shen, J., Zafeiriou, S. P., Chrysos, G. G., Kossaifi, J., Tzimiropoulos, G., & Pantić, M. (2015) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2015.132 · Cited by 289 · see citing works
Deep Spatial Gradient and Temporal Depth Learning for Face Anti-Spoofing
Wang, Z., Yu, Z., Zhao, C., et al. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00509 · Cited by 240 · see citing works
Dong, X., Yu, S.-I., Weng, X., Wei, S.-E., Yang, Y., & Sheikh, Y. A. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00045 · Cited by 216 · see citing works
Self-citationGlobal supervised descent method
Xiong, X., & la Torre, F. D. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298882 · Cited by 191 · see citing works
The florence 2D/3D hybrid face dataset
Bagdanov, A. D., Bimbo, A. D., & Masi, I. (2011) · doi:10.1145/2072572.2072597 · Cited by 177 · see citing works
Face Anti-Spoofing with Human Material Perception
Yu, Z., Li, X., Niu, X., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58571-6_33 · Cited by 172 · see citing works
Learning Meta Face Recognition in Unseen Domains
Guo, J., Zhu, X., Zhao, C., Cao, D., Lei, Z., & Li, S. Z. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00620 · Cited by 162 · see citing works
Self-citationJoint Face Alignment and 3D Face Reconstruction
Liu, F., Zeng, D., Zhao, Q., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46454-1_33 · Cited by 155 · see citing works
FML: Face Model Learning From Videos
Tewari, A., Bernard, F., Garrido, P., Bharaj, G., Elgharib, M. A., Seidel, H., Pérez, P., Zollhöfer, M., & Theobalt, C. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01107 · Cited by 154 · see citing works
Liu, Y., Jourabloo, A., Ren, W., et al. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.190 · Cited by 150 · see citing works
A Recurrent Encoder-Decoder Network for Sequential Face Alignment
Peng, X., Feris, R., Wang, X., & Metaxas, D. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46448-0_3 · Cited by 148 · see citing works
Yang, C.-Y., Liu, S., & Yang, M. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.146 · Cited by 146 · see citing works
Dominant and Complementary Emotion Recognition From Still Images of Faces
Guo, J., Lei, Z., Wan, J., Avots, E., Hajarolasvadi, N., Knyazev, B. A., Kuharenko, A., Jacques, J. C. S., Baró, X., Demirel, H., et al. (2018) · IEEE Access · doi:10.1109/access.2018.2831927 · Cited by 115 · see citing works
Self-citationBhagavatula, C., Zhu, C., Luu, K., & Savvides, M. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.429 · Cited by 113 · see citing works
Learning Meta Model for Zero- and Few-Shot Face Anti-Spoofing
Qin, Y., Zhao, C., Zhu, X., et al. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6866 · Cited by 102 · see citing works
Self-citationARBEE: Towards Automated Recognition of Bodily Expression of Emotion in the Wild
Luo, Y., Ye, J., Adams, R. B., Li, J., Newman, M. G., & Wang, J. Z. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01215-y · Cited by 101 · see citing works
Towards High Fidelity Face Frontalization in the Wild
Cao, J., Hu, Y., Zhang, H., He, R., & Sun, Z. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01229-6 · Cited by 101 · see citing works
Two-Stream Transformer Networks for Video-Based Face Alignment
Liu, H., Lu, J., Feng, J., & Zhou, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2734779 · Cited by 94 · see citing works
Learning Dense Facial Correspondences in Unconstrained Images
Yu, R., Saito, S., Li, H., Ceylan, D., & Li, H. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.506 · Cited by 76 · see citing works
Towards Highly Accurate and Stable Face Alignment for High-Resolution Videos
Tai, Y. Y., Liang, Y., Liu, X., Duan, L., Li, J., Wang, C., Huang, F., & Chen, Y. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33018893 · Cited by 74 · see citing works
Learning a High Fidelity Pose Invariant Model for High-resolution Face FrontalizationPreprint
Cao, J., Hu, Y., Zhang, H., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.08472 · Cited by 55 · see citing works
Neural Style-preserving Visual Dubbing
Kim, H., Elgharib, M., Zollhöfer, M., et al. (2019) · MPG.PuRe (Max Planck Society) · Cited by 50 · see citing works
The 3D Menpo Facial Landmark Tracking Challenge
Zafeiriou, S. P., Chrysos, G. G., Roussos, A., et al. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.16 · Cited by 47 · see citing works
Face Synthesis for Eyeglass-Robust Face Recognition
Guo, J., Zhu, X., Lei, Z., & Li, S. Z. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-97909-0_30 · Cited by 46 · see citing works
Self-citationImproving Face Anti-Spoofing by 3D Virtual Synthesis
Guo, J., Zhu, X., Xiao, J., et al. (2019) · International Conference on Biometrics · doi:10.1109/icb45273.2019.8987415 · Cited by 39 · see citing works
Self-citationMulti-modality Network with Visual and Geometrical Information for Micro Emotion Recognition
Guo, J., Zhou, S., Wu, J., Wan, J., Zhu, X., Lei, Z., & Li, S. Z. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.103 · Cited by 38 · see citing works
Self-citationStabilized real-time face tracking via a learned dynamic rigidity prior
Cao, C., Chai, M., Woodford, O. J., et al. (2018) · ACM Transactions on Graphics · doi:10.1145/3272127.3275093 · Cited by 38 · see citing works
Beyond 3DMM Space: Towards Fine-Grained 3D Face Reconstruction
Zhu, X., Yang, F., Huang, D., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58598-3_21 · Cited by 37 · see citing works
Self-citationMobileFace: 3D Face Reconstruction with Efficient CNN Regression
Chinaev, N., Chigorin, A., & Laptev, I. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11018-5_3 · Cited by 32 · see citing works
Informative Sample Mining Network for Multi-domain Image-to-Image Translation
Cao, J., Huang, H., Li, Y., He, R., & Sun, Z. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58529-7_24 · Cited by 10 · see citing works
Face Expression Recognition and Analysis: The State of the Art
Bettadapura, V. (2012) · arXiv · doi:10.48550/arxiv.1203.6722
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- Authors
- Guo, J., Zhu, X., Yang, Y., Yang, F., Lei, Z., & Li, S. Z.
- Affiliations
- Chinese Academy of Sciences (CN)
- Beihang University (CN)
- Westlake University (CN)
- Corresponding author
- Lei, Zhen
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 152-168
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3104300620
- Cited by
- 462
- References
- 59
- Citations by year
- 2026: 37 · 2025: 69 · 2024: 84 · 2023: 114 · 2022: 89 · 2021: 65 · 2020: 3 · 2019: 1
- FWCI
- 20.90
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Keywords
- 3D face alignment, 3D morphable model
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