Virtual Face Animation Generation Based on Conditional Generative Adversarial Networks

Zeng, Jia; He, Xiangzhen; Li, Shuaishuai; Wu, Lindong; Wang, Jiaxin

doi:10.1109/icicml57342.2022.10009693

2022 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) 2022

DOI: 10.1109/icicml57342.2022.10009693

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Virtual Face Animation Generation Based on Conditional Generative Adversarial Networks

Jia Zeng

Xiangzhen He

Shuaishuai Li

et al.

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2024

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Cited by 2 publications

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Feature Weighted Cycle Generative Adversarial Network with Facial Landmark Recognition and Perceptual Color Distance for Enhanced Face Animation Generation

Lo,

Cheng,

2024

Electronics

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We propose an anime style transfer model to generate anime faces from human face images. We improve the model by modifying the normalization function to obtain more feature information. To make the face feature position of the anime face similar to the human face, we propose facial landmark loss to calculate the error between the generated image and the real human face image. To avoid obvious color deviation in the generated images, we introduced perceptual color loss into the loss function. In addition, due to the lack of reasonable metrics to evaluate the quality of the animated images, we propose the use of Fréchet anime inception distance to calculate the distance between the distribution of the generated animated images and the real animated images in high-dimensional space, so as to understand the quality of the generated animated images. In the user survey, up to 74.46% of users think that the image produced by the proposed method is the best compared with other models. Also, the proposed method reaches a score of 126.05 for Fréchet anime inception distance. Our model performs the best in both user studies and FAID, showing that we have achieved better performance in human visual perception and model distribution. According to the experimental results and user feedback, our proposed method can generate results with better quality compared to existing methods.

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Feature Weighted Cycle Generative Adversarial Network with Facial Landmark Recognition and Perceptual Color Distance for Enhanced Face Animation Generation

Lo,

Cheng,

2024

Electronics

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Research on Animation Character Expression Generation Based on Attention Conditioned Cyclegan

Kong

2024

Journal of Cases on Information Technology

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With advancements in computer vision and AI, deep learning is applied in animation and game design to enhance facial expression realism. This study proposes ACCGAN, an innovative method for automated animated character expression generation and transfer using deep learning. By integrating cycle consistency, conditional generation, and attention mechanisms, ACCGAN optimizes GANs in capturing complex expression features, maintaining naturalness, and accurately expressing details. ACCGAN utilizes multi-level attention mechanisms, dynamically adjusting focus on key facial features, enhancing capture and expression.

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Virtual Face Animation Generation Based on Conditional Generative Adversarial Networks

Cited by 2 publications

References 5 publications

Feature Weighted Cycle Generative Adversarial Network with Facial Landmark Recognition and Perceptual Color Distance for Enhanced Face Animation Generation

Feature Weighted Cycle Generative Adversarial Network with Facial Landmark Recognition and Perceptual Color Distance for Enhanced Face Animation Generation

Research on Animation Character Expression Generation Based on Attention Conditioned Cyclegan

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