The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Generative Face Completion
Open accessAbstract
In this paper, we propose an effective face completion algorithm using a deep generative model. Different from well-studied background completion, the face completion task is more challenging as it often requires to generate semantically new pixels for the missing key components (e.g., eyes and mouths) that contain large appearance variations. Unlike existing nonparametric algorithms that search for patches to synthesize, our algorithm directly generates contents for missing regions based on a neural network. The model is trained with a combination of a reconstruction loss, two adversarial losses and a semantic parsing loss, which ensures pixel faithfulness and local-global contents consistency. With extensive experimental results, we demonstrate qualitatively and quantitatively that our model is able to deal with a large area of missing pixels in arbitrary shapes and generate realistic face completion results.
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Citations
Cites
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235
Context Encoders: Feature Learning by Inpainting
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.278 · Cited by 5,691
Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553380 · Cited by 5,366 · cited 1×
“To effectively train our network, we use the curriculum strategy [3] by gradually increasing the difficulty level and network scale.”
Method
Cited by
Image Inpainting for Irregular Holes Using Partial Convolutions
Liu, G., Reda, F. A., Shih, K. J., Wang, T.-C., Tao, A., & Catanzaro, B. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01252-6_6 · Cited by 2,419
“Li et al. [17] and Iizuka et al. [10] extended Content Encoders by defining both global and local discriminators; then Iizuka et al. [10] apply Poisson blending as a post-process.”
BackgroundOcclusion Aware Facial Expression Recognition Using CNN With Attention Mechanism
Li, Y., Zeng, J., Shan, S., & Chen, X. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2886767 · Cited by 916
Self-citationAttentive Generative Adversarial Network for Raindrop Removal from A Single Image
Qian, R., Tan, R. T., Yang, W., Su, J., & Liu, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00263 · Cited by 821
Loyola-Gonzalez, O. (2019) · IEEE Access · doi:10.1109/access.2019.2949286 · Cited by 559
FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals
Ciftci, U. A., Demir, I., & Yin, L. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3009287 · Cited by 499
Metadata
- Authors
- Li, Y., Liu, S., Yang, J., & Yang, M.-H.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 5892-5900
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2611104282
- Cited by
- 645
- References
- 4
- FWCI
- 25.72
- Type
- conference paper
- Language
- en
- Open access
- green
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