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Portrait Fidelity Generation is a prominent research area in generative models.Current methods face challenges in generating full-body images with low-resolution faces, especially in multi-ID photo phenomenon.To tackle these issues, we propose a comprehensive system called FPGA and construct a million-level multi-modal dataset IDZoom for training.FPGA consists of Multi-Mode Fusion training strategy (MMF) and DDIM Inversion based ID Restoration inference framework (DIIR).
2010
Earlier work this paper cites.
Z. Liu, P. Luo, X. Wang, and X. Tang, “Deep learning face attributes in the wild,” in Proceedings of International Conference on Computer Vision (ICCV) , December 2015
2015
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
X. Huang and S. Belongie, “Arbitrary style transfer in real-time with adaptive instance normalization,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 1501–1510
2017
Earlier work this paper cites.
C. Yu, J. Wang, C. Peng, C. Gao, G. Yu, and N. Sang, “Bisenet: Bilateral segmentation network for real-time semantic segmentation,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 325–341
2018
Earlier work this paper cites.
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 4401–4410
2019
Earlier work this paper cites.
Z. Cao, G. Hidalgo Martinez, T. Simon, S. Wei, and Y. A. Sheikh, “Openpose: Realtime multi-person 2d pose estimation using part affinity fields,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2019
2019
Earlier work this paper cites.
J. Deng, J. Guo, X. Niannan, and S. Zafeiriou, “Arcface: Additive angular margin loss for deep face recognition,” in CVPR , 2019
2019
Earlier work this paper cites.
J. Deng, J. Guo, T. Liu, M. Gong, and S. Zafeiriou, “Sub-center arcface: Boosting face recognition by large-scale noisy web faces,” in Proceedings of the IEEE Conference on European Conference on Computer Vision , 2020
2020
Earlier work this paper cites.
X. Ding, X. Zhang, N. Ma, J. Han, G. Ding, and J. Sun, “Repvgg: Making vgg-style convnets great again,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 13 733–13 742
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
H. Jin, S. Liao, and L. Shao, “Pixel-in-pixel net: Towards efficient facial landmark detection in the wild,” International Journal of Computer Vision , Sep 2021. [Online]. Available: http://dx.doi.org/10.1007/s11263-021-01521-4
2021
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever, “Learning transferable visual models from natural language supervision,” CoRR , 2021
2021
Earlier work this paper cites.
2022
Cited alongside, same era.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695
2022
Cited alongside, same era.
Y. Zheng, H. Yang, T. Zhang, J. Bao, D. Chen, Y. Huang, L. Yuan, D. Chen, M. Zeng, and F. Wen, “General facial representation learning in a visual-linguistic manner,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 18 697–18 709
2022
Cited alongside, same era.
S. Zhou, K. Chan, C. Li, and C. C. Loy, “Towards robust blind face restoration with codebook lookup transformer,” Advances in Neural Information Processing Systems , vol. 35, pp. 30 599–30 611, 2022
2022
A. Wang, H. Chen, Z. Lin, J. Han, and G. Ding, “Repvit: Revisiting mobile cnn from vit perspective,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2024, pp. 15 909–15 920
2024
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Z. Li, M. Cao, X. Wang, Z. Qi, M.-M. Cheng, and Y. Shan, “Photomaker: Customizing realistic human photos via stacked id embedding,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2024
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