LGA-GAN: landmarks guided attentive generative adversarial network for facial expression manipulation

Jiashu Zhu; Junfei Huang

doi:10.1117/12.2626924

2 March 2022 LGA-GAN: landmarks guided attentive generative adversarial network for facial expression manipulation

Jiashu Zhu, Junfei Huang

Proceedings Volume 12158, International Conference on Computer Vision and Pattern Analysis (ICCPA 2021); 1215809 (2022) https://doi.org/10.1117/12.2626924
Event: 2021 International Conference on Computer Vision and Pattern Analysis, 2021, Guangzhou, China

Abstract

Recent advances in Generative Adversarial Networks (GANs) have shown impressive improvements for facial expression manipulation. However, previous methods still generate undesired artifacts and blurs in large-gap and large-angle situations. To address these problems, we propose a novel Landmark Guided Attentive GAN (LGA-GAN). A novel Expression Extraction Network (EENet) is proposed to extract expression-related features. At the heart of our method is a new landmark guided attentive (LGA) matrix that calculates where the expression of a pixel in the reference image should be applied in the synthesized result. With the help of LGA matrix and source image, the Expression Injection Network (EINet) decodes the transferred feature and outputs the synthesized image. Extensive experiments on both quantitative and qualitative evaluation demonstrate the improvements of our proposed approach.

Citation Download Citation

Jiashu Zhu and Junfei Huang "LGA-GAN: landmarks guided attentive generative adversarial network for facial expression manipulation", Proc. SPIE 12158, International Conference on Computer Vision and Pattern Analysis (ICCPA 2021), 1215809 (2 March 2022); https://doi.org/10.1117/12.2626924

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KEYWORDS

Visualization

Networks

Feature extraction

Matrices

3D modeling

Image fusion

Image quality

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