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<p>The rapid development of modern technology and civilization has made the survival of non-heritage culture more and more serious, and the protection and inheritance of intangible cultural heritage is a heavy task and a long way to go. This paper takes the feasibility of non-heritage animation creation as an entry point, analyzes the ideological mechanism in the process of non-heritage animation creation, and explores the economic realization brought by deep learning technology assisting non-heritage animation creation. For the lens scene switching in the process of non-heritage animation creation, this paper utilizes the CNN network for the initial positioning of the lens boundary. It establishes the tangent detection model of a non-heritage animation lens by combining it with the 3D-CNN network. To understand the diversity of non-heritage animation creation styles, this paper establishes a model for style migration of non-heritage cultural images based on the VGG-Net network and conducts experimental investigations. The results show that when the hyperparameter value of the model is set to <italic>λ</italic> = 3, <italic>γ</italic> = 1.2, the model retains only 5.19% of the candidate boundary frames, and the accuracy of the detection of the non-heritage animation creation tangent shots is 0.939. The total loss value in the process of style migration fluctuates around 0.005, and the subjective evaluation score of the images generated by the style migration network is 4.82. The deep learning algorithm that promotes the creation of non-heritage animation can expand the content and characteristics of non-heritage animation creation and can also realize the economic realization of non-heritage animation.</p>
</abstract>

The rapid development of modern technology and civilization has made the survival of non-heritage culture more and more serious, and the protection and inheritance of intangible cultural heritage is a heavy task and a long way to go. This paper takes the feasibility of non-heritage animation creation as an entry point, analyzes the ideological mechanism in the process of non-heritage animation creation, and explores the economic realization brought by deep learning technology assisting non-heritage animation creation. For the lens scene switching in the process of non-heritage animation creation, this paper utilizes the CNN network for the initial positioning of the lens boundary. It establishes the tangent detection model of a non-heritage animation lens by combining it with the 3D-CNN network. To understand the diversity of non-heritage animation creation styles, this paper establishes a model for style migration of non-heritage cultural images based on the VGG-Net network and conducts experimental investigations. The results show that when the hyperparameter value of the model is set to λ = 3, γ = 1.2, the model retains only 5.19% of the candidate boundary frames, and the accuracy of the detection of the non-heritage animation creation tangent shots is 0.939. The total loss value in the process of style migration fluctuates around 0.005, and the subjective evaluation score of the images generated by the style migration network is 4.82. The deep learning algorithm that promotes the creation of non-heritage animation can expand the content and characteristics of non-heritage animation creation and can also realize the economic realization of non-heritage animation.

The Practice and exploration of deep learning algorithm in the creation and realization of intangible cultural heritage animation

Applied Mathematics and Nonlinear Sciences

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

The rapid development of modern technology and civilization has made the survival of non-heritage culture more and more serious, and the protection and...

The Practice and exploration of deep learning algorithm in the creation and realization of intangible cultural heritage animation

Online veröffentlicht: 07. Juni 2024

Seitenbereich: -

Eingereicht: 13. Feb. 2024

Akzeptiert: 20. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1418

Schlüsselwörter
Deep learning algorithm, 3D-CNN, VGG-Net, Style migration, Lens boundary detection, Non-heritage animation

© 2024 Ran Tao, published by Sciendo

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

The Practice and exploration of deep learning algorithm in the creation and realization of intangible cultural heritage animation

Online veröffentlicht: 07. Juni 2024

Seitenbereich: -

Eingereicht: 13. Feb. 2024

Akzeptiert: 20. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1418

SchlüsselwörterDeep learning algorithm, 3D-CNN, VGG-Net, Style migration, Lens boundary detection, Non-heritage animation

© 2024 Ran Tao, published by Sciendo

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

Schlüsselwörter
Deep learning algorithm, 3D-CNN, VGG-Net, Style migration, Lens boundary detection, Non-heritage animation