Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/146680
Title: | Cascade EF-GAN : progressive facial expression editing with local focuses | Authors: | Wu, Rongliang Zhang, Gongjie Lu, Shijian Chen, Tao |
Keywords: | Engineering | Issue Date: | 2020 | Source: | Wu, R., Zhang, G., Lu, S., & Chen, T. (2020). Cascade EF-GAN : progressive facial expression editing with local focuses. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1, 5020-5029. doi:10.1109/CVPR42600.2020.00507 | Project: | #001531-00001 | Conference: | 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) | Abstract: | Recent advances in Generative Adversarial Nets (GANs) have shown remarkable improvements for facial expression editing. However, current methods are still prone to generate artifacts and blurs around expression-intensive regions, and often introduce undesired overlapping artifacts while handling large-gap expression transformations such as transformation from furious to laughing. To address these limitations, we propose Cascade Expression Focal GAN (Cascade EF-GAN), a novel network that performs progressive facial expression editing with local expression focuses. The introduction of the local focus enables the Cascade EF-GAN to better preserve identity-related features and details around eyes, noses and mouths, which further helps reduce artifacts and blurs within the generated facial images. In addition, an innovative cascade transformation strategy is designed by dividing a large facial expression transformation into multiple small ones in cascade, which helps suppress overlapping artifacts and produce more realistic editing while dealing with large-gap expression transformations. Extensive experiments over two publicly available facial expression datasets show that our proposed Cascade EF-GAN achieves superior performance for facial expression editing. | URI: | https://hdl.handle.net/10356/146680 | ISBN: | 978-1-7281-7168-5 | ISSN: | 2575-7075 | DOI: | 10.1109/CVPR42600.2020.00507 | Schools: | School of Computer Science and Engineering | Research Centres: | Data Science and Artificial Intelligence Research Centre | Rights: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work is available at: https://doi.org/10.1109/CVPR42600.2020.00507 | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | SCSE Conference Papers |
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