Fetching the paper…
Reading the bibliography…
Diffusion-based technologies have made significant strides, particularly in personalized and customized facialgeneration.
N. Tumanyan, M. Geyer, S. Bagon, and T. Dekel, “Plug-and-play diffusion features for text-driven image-to-image translation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 1921–1930
1930
Earlier work this paper cites.
N. Kumari, B. Zhang, R. Zhang, E. Shechtman, and J.-Y. Zhu, “Multi-concept customization of text-to-image diffusion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 1931–1941
1941
Earlier work this paper cites.
2014
Earlier work this paper cites.
Z. Liu, P. Luo, X. Wang, and X. Tang, “Deep learning face attributes in the wild,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 3730–3738
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18 . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823
2015
Earlier work this paper cites.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
B. Wang, H. Zheng, X. Liang, Y. Chen, L. Lin, and M. Yang, “Toward characteristic-preserving image-based virtual try-on network,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 589–604
2018
Earlier work this paper cites.
Q. Cao, L. Shen, W. Xie, O. M. Parkhi, and A. Zisserman, “Vggface2: A dataset for recognising faces across pose and age,” in 2018 13th IEEE international conference on automatic face & gesture recognition (FG 2018) . IEEE, 2018, pp. 67–74
2018
Earlier work this paper cites.
L. Wan, J. Wan, Y. Jin, Z. Tan, and S. Z. Li, “Fine-grained multi-attribute adversarial learning for face generation of age, gender and ethnicity,” in 2018 International Conference on Biometrics (ICB) . IEEE, 2018, pp. 98–103
2018
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.
O. R. Nasir, S. K. Jha, M. S. Grover, Y. Yu, A. Kumar, and R. R. Shah, “Text2facegan: Face generation from fine grained textual descriptions,” in 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM) . IEEE, 2019, pp. 58–67
2019
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.
J. Deng, J. Guo, N. Xue, and S. Zafeiriou, “Arcface: Additive angular margin loss for deep face recognition,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 4690–4699
2019
Earlier work this paper cites.
2020
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems , vol. 33, pp. 6840–6851, 2020
2020
Earlier work this paper cites.
K. Wang, Q. Wu, L. Song, Z. Yang, W. Wu, C. Qian, R. He, Y. Qiao, and C. C. Loy, “Mead: A large-scale audio-visual dataset for emotional talking-face generation,” in European Conference on Computer Vision . Springer, 2020, pp. 700–717
2020
Earlier work this paper cites.
Z. Huang and Y. Li, “Interpretable and accurate fine-grained recognition via region grouping,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 8662–8672
2020
Earlier work this paper cites.
W. Cong, J. Zhang, L. Niu, L. Liu, Z. Ling, W. Li, and L. Zhang, “Dovenet: Deep image harmonization via domain verification,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 8394–8403
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
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 et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Earlier work this paper cites.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2022
Cited alongside, same era.
C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans et al. , “Photorealistic text-to-image diffusion models with deep language understanding,” Advances in Neural Information Processing Systems , vol. 35, pp. 36 479–36 494, 2022
2022
2023
Later among the works it cites.
2023
Later among the works it cites.
W. Peebles and S. Xie, “Scalable diffusion models with transformers,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 4195–4205
2023
Later among the works it cites.
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
2022
Cited alongside, same era.
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.
Y. Nitzan, K. Aberman, Q. He, O. Liba, M. Yarom, Y. Gandelsman, I. Mosseri, Y. Pritch, and D. Cohen-Or, “Mystyle: A personalized generative prior,” ACM Transactions on Graphics (TOG) , vol. 41, no. 6, pp. 1–10, 2022
2022
Cited alongside, same era.
S. Umirzakova and T. K. Whangbo, “Detailed feature extraction network-based fine-grained face segmentation,” Knowledge-Based Systems , vol. 250, p. 109036, 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
D. Beniaguev, “Synthetic faces high quality (sfhq) dataset,” 2022. [Online]. Available: https://github.com/SelfishGene/SFHQ-dataset
2022
Cited alongside, same era.
2023
Later among the works it cites.
N. Ruiz, Y. Li, V. Jampani, Y. Pritch, M. Rubinstein, and K. Aberman, “Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 22 500–22 510
2023
Later among the works it cites.
Z. Liu, M. Li, Y. Zhang, C. Wang, Q. Zhang, J. Wang, and Y. Nie, “Fine-grained face swapping via regional gan inversion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 8578–8587
2023
Later among the works it cites.
F. Rosberg, E. E. Aksoy, F. Alonso-Fernandez, and C. Englund, “Facedancer: pose-and occlusion-aware high fidelity face swapping,” in Proceedings of the IEEE/CVF winter conference on applications of computer vision , 2023, pp. 3454–3463
2023
Later among the works it cites.
C. Yu, G. Lu, Y. Zeng, J. Sun, X. Liang, H. Li, Z. Xu, S. Xu, W. Zhang, and H. Xu, “Towards high-fidelity text-guided 3d face generation and manipulation using only images,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 15 326–15 337
2023
Later among the works it cites.
L. Li, T. Zhang, Z. Kang, and X. Jiang, “Mask-fpan: Semi-supervised face parsing in the wild with de-occlusion and uv gan,” Computers & Graphics , vol. 116, pp. 185–193, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
H. Chefer, Y. Alaluf, Y. Vinker, L. Wolf, and D. Cohen-Or, “Attend-and-excite: Attention-based semantic guidance for text-to-image diffusion models,” ACM Transactions on Graphics (TOG) , vol. 42, no. 4, pp. 1–10, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
M. Stypułkowski, K. Vougioukas, S. He, M. Zięba, S. Petridis, and M. Pantic, “Diffused heads: Diffusion models beat gans on talking-face generation,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2024, pp. 5091–5100
2024
Closest in time.
Z. Xue, G. Song, Q. Guo, B. Liu, Z. Zong, Y. Liu, and P. Luo, “Raphael: Text-to-image generation via large mixture of diffusion paths,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
2024
Closest in time.
J. Gu, Y. Wang, N. Zhao, T.-J. Fu, W. Xiong, Q. Liu, Z. Zhang, H. Zhang, J. Zhang, H. Jung et al. , “Photoswap: Personalized subject swapping in images,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
2024
Closest in time.
Z. Qiu, W. Liu, H. Feng, Y. Xue, Y. Feng, Z. Liu, D. Zhang, A. Weller, and B. Schölkopf, “Controlling text-to-image diffusion by orthogonal finetuning,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
W. Chen, H. Hu, Y. Li, N. Ruiz, X. Jia, M.-W. Chang, and W. W. Cohen, “Subject-driven text-to-image generation via apprenticeship learning,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.