<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Under the multiple driving of capital, new media and consumer group, the output value of Chinese animation industry keeps growing rapidly. Meanwhile, the quality of animation characters image determines the overall development level of the animation industry. This paper proposed a detail feature preservation-based 3D facial expression transfer method aiming at the design of 3D characters. By extracting the detail features of 3D facial model, the basic expression after removing the details was obtained. By using the improved joint learning method, the basic expression of the source model was transferred to the target model, and the feature modulus based on the Laplace factor was constructed. Finally, through the detail feature vector adjustment strategy, the target model with source basic expression was performed detail restoration. The multiple experiments show that the method proposed in this paper can transfer the expression of the source model to the target model without damage, and at the same time, preserve the personality detail features of the target model. Moreover, the animation expressions generated are real and natural, and the facial micro-movements of animation characters are rich, which positively affects the development of Chinese animation industry.</p>
</abstract>

Under the multiple driving of capital, new media and consumer group, the output value of Chinese animation industry keeps growing rapidly. Meanwhile, the quality of animation characters image determines the overall development level of the animation industry. This paper proposed a detail feature preservation-based 3D facial expression transfer method aiming at the design of 3D characters. By extracting the detail features of 3D facial model, the basic expression after removing the details was obtained. By using the improved joint learning method, the basic expression of the source model was transferred to the target model, and the feature modulus based on the Laplace factor was constructed. Finally, through the detail feature vector adjustment strategy, the target model with source basic expression was performed detail restoration. The multiple experiments show that the method proposed in this paper can transfer the expression of the source model to the target model without damage, and at the same time, preserve the personality detail features of the target model. Moreover, the animation expressions generated are real and natural, and the facial micro-movements of animation characters are rich, which positively affects the development of Chinese animation industry.

Influence of 3D characters image transfer on animation drawing based on detail feature preservation

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Under the multiple driving of capital, new media and consumer group, the output value of Chinese animation industry keeps growing rapidly. Meanwhile, the...

Influence of 3D characters image transfer on animation drawing based on detail feature preservation

Online veröffentlicht: 02. Juni 2023

Seitenbereich: -

Eingereicht: 19. Juni 2022

Akzeptiert: 21. Okt. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00187

Schlüsselwörter
Animation character image, Detail features, Laplace factor, Facial expression transfer, Joint learning

© 2023 Linye Tang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Influence of 3D characters image transfer on animation drawing based on detail feature preservation

Online veröffentlicht: 02. Juni 2023

Seitenbereich: -

Eingereicht: 19. Juni 2022

Akzeptiert: 21. Okt. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00187

SchlüsselwörterAnimation character image, Detail features, Laplace factor, Facial expression transfer, Joint learning

© 2023 Linye Tang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Animation character image, Detail features, Laplace factor, Facial expression transfer, Joint learning