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<p>Artificial Intelligence Generated Content (AIGC) technology aligns seamlessly with the design requirements of non-genetic heritage, offering a viable pathway for its modernization. This paper delineates the specific design needs of non-genetic heritage and utilizes a diffusion model to create themed images and animations related to this heritage. Additionally, AIGC is employed to enhance the creation of virtual reality interactive imagery. The Long Short-Term Memory (LSTM) network is deployed to classify time-series gesture data, facilitating the training and categorization of Chinese Sign Language (CSL) gestures for virtual interactive engagement with non-heritage themes. We have integrated the AIGC operation process into the theme of non-genetic inheritance, thereby constructing a robust development trajectory for AIGC-enhanced non-genetic heritage. The experimental setup is crafted to ascertain the optimal number of iterations and training durations through the control variable method. We evaluate the efficacy of the diffusion model for anti-implicit writing analysis and the performance of the speech recognition, text dialogue, and text response modules within the non-heritage multimodal interactive framework using Word Error Rate (WER) and Mean Opinion Score (MOS). A descriptive analysis of users’ interactive experiences with non-heritage content is also conducted. The results indicate that the speech recognition module achieved a WER of 0.365, while the text response module garnered an MOS of 4.49 with a standard deviation of 0.56. This multimodal, non-heritage virtual interaction, leveraging multiple modalities, enriches users’ experiences and deepens their understanding and appreciation of non-heritage content. Consequently, this enhances the high-quality development of non-genetic heritage.</p>
</abstract>

Artificial Intelligence Generated Content (AIGC) technology aligns seamlessly with the design requirements of non-genetic heritage, offering a viable pathway for its modernization. This paper delineates the specific design needs of non-genetic heritage and utilizes a diffusion model to create themed images and animations related to this heritage. Additionally, AIGC is employed to enhance the creation of virtual reality interactive imagery. The Long Short-Term Memory (LSTM) network is deployed to classify time-series gesture data, facilitating the training and categorization of Chinese Sign Language (CSL) gestures for virtual interactive engagement with non-heritage themes. We have integrated the AIGC operation process into the theme of non-genetic inheritance, thereby constructing a robust development trajectory for AIGC-enhanced non-genetic heritage. The experimental setup is crafted to ascertain the optimal number of iterations and training durations through the control variable method. We evaluate the efficacy of the diffusion model for anti-implicit writing analysis and the performance of the speech recognition, text dialogue, and text response modules within the non-heritage multimodal interactive framework using Word Error Rate (WER) and Mean Opinion Score (MOS). A descriptive analysis of users’ interactive experiences with non-heritage content is also conducted. The results indicate that the speech recognition module achieved a WER of 0.365, while the text response module garnered an MOS of 4.49 with a standard deviation of 0.56. This multimodal, non-heritage virtual interaction, leveraging multiple modalities, enriches users’ experiences and deepens their understanding and appreciation of non-heritage content. Consequently, this enhances the high-quality development of non-genetic heritage.

AIGC Enabling Non-Genetic Design Methods and Practices

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"AIGC Enabling Non-Genetic Design Methods and Practices"}

Artificial Intelligence Generated Content (AIGC) technology aligns seamlessly with the design requirements of non-genetic heritage, offering a viable pathway...

AIGC Enabling Non-Genetic Design Methods and Practices

Pubblicato online: 03 giu 2024

Pagine: -

Ricevuto: 07 mar 2024

Accettato: 06 mag 2024

DOI: https://doi.org/10.2478/amns-2024-1319

Parole chiave
AIGC, Diffusion model, Virtual reality interaction, LSTM network, Non-genetic heritage

© 2024 Zujian Li, et al., published by Sciendo

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

AIGC Enabling Non-Genetic Design Methods and Practices

Pubblicato online: 03 giu 2024

Pagine: -

Ricevuto: 07 mar 2024

Accettato: 06 mag 2024

DOI: https://doi.org/10.2478/amns-2024-1319

Parole chiaveAIGC, Diffusion model, Virtual reality interaction, LSTM network, Non-genetic heritage

© 2024 Zujian Li, et al., published by Sciendo

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

Parole chiave
AIGC, Diffusion model, Virtual reality interaction, LSTM network, Non-genetic heritage