Generative AI Shanshui animation enhancement using Perlin noise and diffusion models
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Issued Date
2026-12-01
Resource Type
eISSN
27310809
Scopus ID
2-s2.0-105029636764
Journal Title
Discover Artificial Intelligence
Volume
6
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Discover Artificial Intelligence Vol.6 No.1 (2026)
Suggested Citation
Wattanachote K., Lin C.Y., Hsu S.E., Shih T.K. Generative AI Shanshui animation enhancement using Perlin noise and diffusion models. Discover Artificial Intelligence Vol.6 No.1 (2026). doi:10.1007/s44163-025-00797-6 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115094
Title
Generative AI Shanshui animation enhancement using Perlin noise and diffusion models
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Author's Affiliation
Corresponding Author(s)
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Abstract
Deep learning models have achieved remarkable advancements in image generation but face persistent challenges in synthesizing traditional Shanshui (mountain-water) landscape paintings due to limited domain-specific training data and the complexity of aesthetic principles. This study integrated Perlin Noise, Stable Diffusion, ControlNet, and AnimateDiff to enhance Shanshui landscape generation and animation. Perlin Noise constructs naturalistic skeletal structures, which are further refined using ControlNet for precise structural control. Advanced prompt engineering with GPT-4 and Textual Inversion improved prompt descriptiveness and mitigated low-quality outputs. Furthermore, LoRA fine-tuning improved the adaptability of our Shanshui landscapes model. Integrating I2V Encoders and AnimateDiff enabled the seamless transformation of static landscape images into dynamic animations, preserving artistic authenticity while introducing motion consistency. The experimental results demonstrated significant improvements in realism, stylistic fidelity, and diversity, addressing key limitations in existing generative approaches. This framework not only advances the field of generative AI in digital art but also offers new opportunities for the creation of multimedia content and cultural preservation through the synthesis of computational Shanshui animation.