References(30)
[1]
Y. B. Hu, X. Wu, B. Yu, R. He, and Z. Sun, Pose-guided photorealistic face rotation, in IEEE/CVF Conf. Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 8398-8406.
[2]
T. Y. Yang, Y. T. Chen, Y. Y. Lin, and Y. Y. Chuang, FSA-Net: Learning fine-grained structure aggregation for head pose estimation from a single image, in IEEE/CVF Conf. Computer Vision and Pattern Recognition, Long Beach, CA, USA, 2019, pp. 1087-1096.
[3]
B. Chaudhuri, N. Vesdapunt, and B. Y. Wang, Joint face detection and facial motion retargeting for multiple faces, in IEEE/CVF Conf. Computer Vision and Pattern Recognition, Long Beach, CA, USA, 2019, pp. 9719-9728.
[4]
B. Gecer, S. Ploumpis, I. Kotsia, and S. Zafeiriou, GANFIT: Generative adversarial network fitting for high fidelity 3D face reconstruction, in IEEE/CVF Conf. Computer Vision and Pattern Recognition, Long Beach, CA, USA, 2019, pp. 1155-1164.
[5]
F. Z. Wu, L. C. Bao, Y. J. Chen, Y. G. Ling, Y. B. Song, S. N. Li, K. N. Ngan, and W. Liu, MVF-Net: Multi-view 3D face morphable model regression, in IEEE/CVF Conf. Computer Vision and Pattern Recognition, Long Beach, CA, USA, 2019, pp. 959-968.
[6]
G. P. Meyer, S. Gupta, I. Frosio, D. Reddy, and J. Kautz, Robust model-based 3D head pose estimation, in Proc. IEEE Int. Conf. Computer Vision, Santiago, Chile, 2015, pp. 3649-3657.
[7]
J. Thies, M. Zollhöfer, M. Stamminger, C. Theobalt, and M. Nießner, Face2Face: Real-time face capture and reenactment of RGB videos, in IEEE Conf. Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016, pp. 2387-2395.
[8]
C. Cao, M. L. Chai, O. Woodford, and L. J. Luo, Stabilized real-time face tracking via a learned dynamic rigidity prior, ACM Trans. Graph., vol. 37, no. 6, p. 233, 2018.
[9]
J. M. Saragih, S. Lucey, and J. F. Cohn, Deformable model fitting by regularized landmark mean-shift, Int. J. Comput. Vis., vol. 91, no. 2, pp. 200-215, 2011.
[10]
M. Kocabas, S. Karagoz, and E. Akbas, Self-supervised learning of 3D human pose using multi-view geometry, in IEEE/CVF Conf. Computer Vision and Pattern Recognition, Long Beach, CA, USA, 2019, pp. 1077-1086.
[11]
L. H. Ge, Z. Ren, Y. C. Li, Z. H. Xue, Y. Y. Wang, J. F. Cai, and J. S. Yuan, 3D hand shape and pose estimation from a single RGB image, in IEEE/CVF Conf. Computer Vision and Pattern Recognition, Long Beach, CA, USA, 2019, pp. 10833-10842.
[12]
C. Wang, D. F. Xu, Y. K. Zhu, R. Martín-Martín, C. W. Lu, F. F. Li, and S. Savarese, DenseFusion: 6D object pose estimation by iterative dense fusion, in IEEE/CVF Conf. Computer Vision and Pattern Recognition, Long Beach, CA, USA, 2019, pp. 3343-3352.
[13]
S. D. Peng, Y. Liu, Q. X. Huang, X. W. Zhou, and H. J. Bao, PVNet: Pixel-wise voting network for 6DoF pose estimation, in IEEE/CVF Conf. Computer Vision and Pattern Recognition, Long Beach, CA, USA, 2019, pp. 4561-4570.
[14]
A. Kumar and R. Chellappa, Disentangling 3D pose in a dendritic CNN for unconstrained 2D face alignment, in IEEE/CVF Conf. Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 430-439.
[15]
K. D. Cao, Y. Rong, C. Li, X. O. Tang, and C. C. Loy, Pose-robust face recognition via deep residual equivariant mapping, in IEEE/CVF Conf. Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 5187-5196.
[16]
H. Abbas, Y. Hicks, D. Marshall, A. I. Zhurov, and S. Richmond, A 3D morphometric perspective for facial gender analysis and classification using geodesic path curvature features, Comput. Vis. Media, vol. 4, no. 1, pp. 17-32, 2018.
[17]
Y. Xiang, A. Alahi, and S. Savarese, Learning to track: Online multi-object tracking by decision making, in Proc. IEEE Int. Conf. Computer Vision, Santiago, Chile, 2015, pp. 4705-4713.
[18]
A. Crivellaro, M. Rad, Y. Verdie, K. M. Yi, P. Fua, and V. Lepetit, Robust 3D object tracking from monocular images using stable parts, IEEE Trans. Pattern Anal. Mach. Intell., vol. 40, no. 6, pp. 1465-1479, 2018.
[19]
R. Girdhar, G. Gkioxari, L. Torresani, M. Paluri, and D. Tran, Detect-and-track: Efficient pose estimation in videos, in IEEE/CVF Conf. Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 350-359.
[20]
Y. Z. Song, R. C. Fan, S. Huang, Z. Zhu, and R. F. Tong, A three-stage real-time detector for traffic signs in large panoramas, Comput. Vis. Media, .
[21]
S. Z. Zhu, C. Li, C. C. Loy, and X. O. Tang, Face alignment by coarse-to-fine shape searching, in IEEE Conf. Computer Vision and Pattern Recognition, Boston, MA, USA, 2015, pp. 4998-5006.
[22]
C. Sagonas, E. Antonakos, G. Tzimiropoulos, S. Zafeiriou, and M. Pantic, 300 faces in-the-wild challenge: Database and results, Image Vis. Comput., vol. 47, pp. 3-18, 2016.
[23]
C. W. Luo, J. Y. Zhang, J. Yu, C. W. Chen, and S. J. Wang, Real-time head pose estimation and face modeling from a depth image, IEEE Trans. Multimed., vol. 21, no. 10, pp. 2473-2481, 2019.
[25]
O. M. Parkhi, A. Vedaldi, and A. Zisserman, Deep face recognition, presented at the British Machine Vision Conference, Swansea, UK, 2015.
[26]
N. Ruiz, E. Chong, and J. M. Rehg, Fine-grained head pose estimation without keypoints, in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA, 2018, pp. 2074-2083.
[27]
V. Kazemi and J. Sullivan, One millisecond face alignmentwith an ensemble of regression trees, in IEEE Conf. Computer Vision and Pattern Recognition, Columbus, OH, USA, 2014, pp. 1867-1874.
[28]
A. Bulat and G. Tzimiropoulos, How far are we from solving the 2D & 3D face alignment problem? (and a dataset of 230,000 3D facial landmarks), in Proc. IEEE Int. Conf. Computer Vision, Venice, Italy, 2017, pp. 1021-1030.
[29]
A. Kumar, A. Alavi, and R. Chellappa, KEPLER: Keypoint and pose estimation of unconstrained faces by learning efficient H-CNN regressors, in Proc. 12th IEEE Int. Conf. Automatic Face & Gesture Recognition, Washington, DC, USA, 2017, pp. 258-265.
[30]
X. Y. Zhu, Z. Lei, X. M. Liu, H. L. Shi, and S. Z. Li, Face alignment across large poses: A 3D solution, in IEEE Conf. Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016, pp. 146-155.