Self-supervised learning for fine-grained monocular 3D face reconstruction in the wild
Author:
Funder
Shanghai Talent Development Funding of China
Science and Technology Projects of National Archives Administration of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00530-024-01436-3.pdf
Reference50 articles.
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2. Xu, R., Wang, K., Deng, C., et al.: Depth map denoising network and lightweight fusion network for enhanced 3d face recognition. Pattern Recognit. 145, 109936 (2024). https://doi.org/10.1016/j.patcog.2023.109936
3. Shahreza, H.O., Marcel, S.: Comprehensive vulnerability evaluation of face recognition systems to template inversion attacks via 3d face reconstruction. IEEE Trans. Pattern Anal. Mach. Intell. 45(12), 14248–14265 (2023)
4. Huang, Z., Chan, K.C., Jiang, Y., Liu, Z.: Collaborative diffusion for multi-modal face generation and editing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6080–6090 (2023)
5. Zhang, L., Qiu, Q., Lin, H., et al.: Dreamface: progressive generation of animatable 3d faces under text guidance. ACM Trans. Graphics 42(4), 1–16 (2023)
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