Algorithms for digital cultural tourism ecological model with biomechanical considerations in VR scene interactions
Abstract
Digital cultural tourism is an emerging form of tourism that presents cultural heritage and tourism resources digitally to users, providing immersive travel experiences. However, traditional methods of constructing virtual scenes often rely on manual modeling, leading to low efficiency and high costs. The existing digital cultural tourism platforms mostly provide static and pre-set content, lacking interaction with users, making it difficult to achieve personalized recommendations and interactive experiences. In response to these issues, this article is based on VR (Virtual Reality) scene intelligent generation and interactive algorithms, and aims to optimize the overall synergy between the presentation of cultural resources and user experience by constructing a digital cultural tourism ecological model. Drawing on biomechanical principles, the study emphasizes the importance of natural user interactions and physical engagement in enhancing the immersive experience. Firstly, the Lindenmayer system (L-system) and parameterized generation rules are used to generate complex natural landscapes and architectural structures. Natural and textured scene details are added using the Perlin noise algorithm. Using GANs (Generative Adversarial Networks) technology, generative and discriminative networks are trained to generate more realistic VR scenes, further enhancing the realism and detail representation of the scenes. At the same time, a gesture recognition technology combining CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory) models, along with a speech recognition algorithm based on DNN (Deep Neural Networks), is adopted to enhance the natural interaction between users and virtual scenes. By combining collaborative filtering algorithms with user behavior data, personalized content recommendations are realized, enhancing user engagement and satisfaction. The efficiency test of scene modeling, the total time required to generate scenes using the model in this article is only 84 hours, which is much lower than manual modeling. In the interactive test, the highest success rate of the model in this article in gesture recognition reaches 94%. The experimental results have verified the advantages of the model in this article in improving scene modeling efficiency and enhancing immersive experiences through biomechanically informed interactions.
References
1. Ammirato S, Felicetti A M, Linzalone R, Carlucci D. Digital business models in cultural tourism. International Journal of Entrepreneurial Behavior & Research, 2022, 28(8): 1940-1961.
2. Kerdpitak C. The effects of innovative management, digital marketing, service quality and supply chain management on performance in cultural tourism business. Uncertain Supply Chain Management, 2022, 10(3): 771-778.
3. Guo K, Fan A, Lehto X, Day J. Immersive digital tourism: the role of multisensory cues in digital museum experiences. Journal of Hospitality & Tourism Research, 2023, 47(6): 1017-1039.
4. Yanti D, Heryadi D Y, Cakranegara P A, Kadyrov M. Developing rural communication through digital innovation for village tourism. Jurnal Studi Komunikasi, 2023, 7(3): 696-712.
5. Preko A, Amoako G K, Dzogbenuku R K, Kosiba J. Digital tourism experience for tourist site revisit: an empirical view from Ghana. Journal of Hospitality and Tourism Insights, 2023, 6(2): 779-796.
6. Marwan J, Utama Z M. The role of digital tourism destination in achieving digital tourism destination performance through digital's role as intervening variables. International Journal of Human Capital Management, 2022, 6(1): 24-37.
7. Gao L, Sun J M, Mo K, Lai Y K, Guibas L J, Yang J. Scenehgn: Hierarchical graph networks for 3d indoor scene generation with fine-grained geometry. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(7): 8902-8919.
8. Wu Z, Li Y, Yan H, Shang T, Sun W, Wang S, et al. Blockfusion: Expandable 3d scene generation using latent tri-plane extrapolation. ACM Transactions on Graphics (TOG), 2024, 43(4): 1-17.
9. Yang X, Zhang L, Feng Z. Personalized tourism recommendations and the E-tourism user experience. Journal of Travel Research, 2024, 63(5): 1183-1200.
10. Zeng Y, Liu L, Xu R. The effects of a virtual reality tourism experience on tourist’s cultural dissemination behavior. Tourism and Hospitality, 2022, 3(1): 314-329.
11. Lim S G. Construction of HMD-based Interactive Realistic Media Algorithm Using L-System. Journal of Korea Multimedia Society, 2020, 23(1): 58-66.
12. Bernard J, McQuillan I. Stochastic L-system inference from multiple string sequence inputs. Soft Computing, 2023, 27(10): 6783-6798.
13. Etherington T R. Perlin noise as a hierarchical neutral landscape model. Web Ecology, 2022, 22(1): 1-6.
14. Andrian R, Permana A A, Kusnadi A. Design Adventure Role Playing Game With Procedural Content Generation Using Perlin Noise Algorithm. J. Theor. Appl. Inf. Technol, 2023, 101(9): 3653-3665.
15. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial networks. Communications of the ACM, 2020, 63(11): 139-144.
16. Gui J, Sun Z, Wen Y, Tao D, Ye J. A review on generative adversarial networks: Algorithms, theory, and applications. IEEE transactions on knowledge and data engineering, 2021, 35(4): 3313-3332.
Copyright (c) 2025 Jing Peng
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.
Submit a Paper
