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Knowledge-graph-guided diffusion for culturally authentic modernization and animation of heritage patterns.
1College of Fine Arts, Beihua University, Jilin City, 132013, Jilin, China.
Scientific Reports
|June 8, 2026
Summary
This study introduces a framework for modernizing cultural heritage patterns into digital media. It balances authenticity and commercial use, enhancing creativity while preserving cultural integrity.
Area of Science:
- Computer Science
- Digital Heritage
- Artificial Intelligence
Background:
- Cultural heritage preservation faces challenges balancing traditional authenticity with modern commercial applications.
- Transforming historical patterns into animatable digital media requires innovative approaches.
Purpose of the Study:
- To present a unified framework for modernizing traditional patterns into animatable digital media.
- To preserve cultural authenticity while enabling commercial utility.
Main Methods:
- Integrating cultural semantic encoding, knowledge graph-guided diffusion models, and multi-view animation synthesis.
- Utilizing a knowledge graph with 487 cultural concepts and 1243 semantic relations.
- Conducting experiments on 10,010 culturally diverse patterns.
Main Results:
- Achieved a Fréchet Inception Distance of 24.3 (7.6% improvement) and Cultural Semantic Similarity of 0.85 (18.1% improvement).
- Expert evaluation yielded high cultural fidelity scores (4.6 ± 0.3/5).
- Demonstrated strong cross-cultural generalization (0.82-0.87 CSS) and 10x model compression for mobile deployment.
Conclusions:
- Established systematic methods for heritage-aware generative systems.
- Culturally explicit constraints enhance creative output and enable commercially viable applications.
- The framework successfully respects traditional artistic expression in digital media modernization.
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