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. 2022 Jun 9:2022:1434599.
doi: 10.1155/2022/1434599. eCollection 2022.

3D Animation Automatic Generation System Design Based on Deep Learning

Yongli Cao  1 , Lei Wan  1 , Lili Shi  1
Affiliations

3D Animation Automatic Generation System Design Based on Deep Learning

Yongli Cao et al. Comput Intell Neurosci. .

Retraction in

Abstract

In the field of 3D animation design and generation, the expression generation method of animation is not obvious due to the lack of image details, which leads to the lack of realism of the generated animation expressions. In order to solve this problem, a deep learning-based animation character expression generation method is proposed. The method, based on the real facial expression images, uses improved deep learning to design cascade classifiers, extracts facial expression feature images from real images, softens image edges, and enhances feature details. The content and style of images are unified, the loss function is designed from the content constraints and style constraints, the judgment network is optimized, and the feature information is fused under the constraints of the loss function to generate the facial expressions of animated characters. The experimental results show that the design based on the feature point location of the improved deep learning expression generation method is accurate, the Pearson correlation coefficient between the input image and the generated image is high, the root mean square error is small, and the realism of the generated facial expression is enhanced.

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Conflict of interest statement

The authors declare that there are no conflicts of interest.

Figures

Figure 1
Figure 1
Reconstruction process of the expression.
Figure 2
Figure 2
Experimental results of body shape feature localization with different expression generation methods.
See this image and copyright information in PMC

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