Morphable face models-an open framework T Gerig, A Morel-Forster, C Blumer, B Egger, M Luthi, S Schönborn, ... 2018 13th IEEE international conference on automatic face & gesture …, 2018 | 316 | 2018 |
Shape Modeling Using Gaussian Process Morphable Models M Lüthi, A Forster, T Gerig, T Vetter Statistical Shape and Deformation Analysis: Methods, Implementation and …, 2017 | 262* | 2017 |
Analyzing and reducing the damage of dataset bias to face recognition with synthetic data A Kortylewski, B Egger, A Schneider, T Gerig, A Morel-Forster, T Vetter Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 157 | 2019 |
Occlusion-aware 3d morphable models and an illumination prior for face image analysis B Egger, S Schönborn, A Schneider, A Kortylewski, A Morel-Forster, ... International Journal of Computer Vision 126, 1269-1287, 2018 | 92 | 2018 |
Empirically analyzing the effect of dataset biases on deep face recognition systems A Kortylewski, B Egger, A Schneider, T Gerig, A Morel-Forster, T Vetter Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 82 | 2018 |
Training deep face recognition systems with synthetic data A Kortylewski, A Schneider, T Gerig, B Egger, A Morel-Forster, T Vetter arXiv preprint arXiv:1802.05891, 2018 | 78 | 2018 |
Markov chain monte carlo for automated face image analysis S Schönborn, B Egger, A Morel-Forster, T Vetter International Journal of Computer Vision 123, 160-183, 2017 | 78 | 2017 |
A monte carlo strategy to integrate detection and model-based face analysis S Schönborn, A Forster, B Egger, T Vetter Pattern Recognition: 35th German Conference, GCPR 2013, Saarbrücken, Germany …, 2013 | 31 | 2013 |
Robust model-based face reconstruction through weakly-supervised outlier segmentation C Li, A Morel-Forster, T Vetter, B Egger, A Kortylewski Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 21 | 2023 |
Background modeling for generative image models S Schönborn, B Egger, A Forster, T Vetter Computer Vision and Image Understanding 136, 117-127, 2015 | 20 | 2015 |
To fit or not to fit: Model-based face reconstruction and occlusion segmentation from weak supervision C Li, A Morel-Forster, T Vetter, B Egger, A Kortylewski arXiv preprint arXiv:2106.09614 3, 2021 | 18 | 2021 |
A closest point proposal for MCMC-based probabilistic surface registration D Madsen, A Morel-Forster, P Kahr, D Rahbani, T Vetter, M Lüthi Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 18 | 2020 |
Occlusion-aware 3D Morphable Face Models. B Egger, A Schneider, C Blumer, A Forster, S Schönborn, T Vetter BMVC 2, 4, 2016 | 18 | 2016 |
Can synthetic faces undo the damage of dataset bias to face recognition and facial landmark detection? A Kortylewski, B Egger, A Morel-Forster, A Schneider, T Gerig, C Blumer, ... arXiv preprint arXiv:1811.08565, 2018 | 17* | 2018 |
Informed mcmc with bayesian neural networks for facial image analysis A Kortylewski, M Wieser, A Morel-Forster, A Wieczorek, S Parbhoo, ... arXiv preprint arXiv:1811.07969, 2018 | 13 | 2018 |
Probabilistic fitting of active shape models A Morel-Forster, T Gerig, M Lüthi, T Vetter Shape in Medical Imaging: International Workshop, ShapeMI 2018, Held in …, 2018 | 13 | 2018 |
Pose normalization for eye gaze estimation and facial attribute description from still images B Egger, S Schönborn, A Forster, T Vetter Pattern Recognition: 36th German Conference, GCPR 2014, Münster, Germany …, 2014 | 12 | 2014 |
Greedy structure learning of hierarchical compositional models A Kortylewski, A Wieczorek, M Wieser, C Blumer, S Parbhoo, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 11 | 2019 |
Human face shape analysis under spherical harmonics illumination considering self occlusion J Zivanov, A Forster, S Schönborn, T Vetter 2013 International conference on biometrics (ICB), 1-8, 2013 | 10 | 2013 |
Generative shape and image analysis by combining Gaussian processes and MCMC sampling A Morel-Forster University_of_Basel, 2016 | 9 | 2016 |