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.