Knowledge-graph-guided diffusion for culturally authentic modernization and animation of heritage patterns

Cultural heritage preservation involves critical trade-offs between traditional authenticity and modern commercial utility in transforming historical patterns into animatable digital media. This paper presents a unified framework integrating cultural semantic encoding, diffusion models guided by knowledge graphs, and multi-view animation synthesis to modernize traditional patterns while preserving cultural authenticity. The approach includes a knowledge graph of 487 cultural concepts with 1243 semantic relations to condition the diffusion generation process. Comprehensive experiments on 10,010 culturally diverse patterns achieve a Fréchet Inception Distance of 24.3, representing a 7.6% improvement over state-of-the-art baselines with a Cultural Semantic Similarity of 0.85, exceeding existing methods by 18.1%. Expert evaluation by 50 cultural specialists confirms cultural fidelity scores of 4.6 ± 0.3 on a 5-point scale. Cross-cultural analysis of generalization confirms strong performance on Chinese, Persian, and Islamic traditions with 0.82-0.87 Cultural Semantic Similarity scores. The framework is successful in deploying on varied hardware platforms achieving 10× model compression on mobile devices with negligible quality degradation. These results establish systematic methods for heritage-aware generative systems, demonstrating that culturally explicit constraints enhance creative output while enabling commercially viable applications that respect traditional artistic expression.