Cascaded Iterative Transformer for Jointly Predicting Facial Landmark, Occlusion Probability and Head Pose
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Link
https://link.springer.com/content/pdf/10.1007/s11263-023-01935-2.pdf
Reference68 articles.
1. Aghli, N., & Ribeiro, E. (2021). A data-driven approach to improve 3D head-pose estimation. In International Symposium on Visual Computing (pp. 546–558).
2. Albiero, V., Chen, X., Yin, X., Pang, G., & Hassner, T. (2021). img2pose: Face alignment and detection via 6dof, face pose estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7617–7627).
3. Bhagavatula, C., Zhu, C., Luu, K., & Savvides, M. (2017). Faster than real-time facial alignment: A 3D spatial transformer network approach in unconstrained poses. In Proceedings of the IEEE International Conference on Computer Vision (pp. 3980–3989).
4. Bisogni, C., Nappi, M., Pero, C., & Ricciardi, S. (2021). Fashe: A fractal based strategy for head pose estimation. IEEE Transactions on Image Processing, 30, 3192–3203.
5. Bulat, A., & Tzimiropoulos, G. (2017). How far are we from solving the 2D & 3D face alignment problem? (and a dataset of 230,000 3D facial landmarks). In Proceedings of the IEEE International Conference on Computer Vision (pp. 1021–1030).
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