Knowledge graph enhanced cross modal generative adversarial network for martial arts motion reconstruction and heritage preservation

This paper presents a novel knowledge graph enhanced cross-modal generative adversarial network (KG-CMGAN) for preserving traditional martial arts techniques. We address the challenges of capturing the complex, multidimensional nature of martial arts by integrating structured domain knowledge with advanced deep learning architectures. Our framework establishes an end-to-end solution that bridges visual, textual, and sequential representations to achieve comprehensive motion reconstruction while preserving stylistic authenticity and semantic meaning. The proposed approach includes a comprehensive martial arts knowledge graph that formalizes domain-specific ontology, a knowledge-guided cross-modal alignment mechanism that effectively integrates heterogeneous data sources, and a knowledge-enhanced adversarial learning architecture specifically optimized for martial arts motion reconstruction. Extensive experiments across six traditional Chinese martial arts styles demonstrate significant improvements over state-of-the-art baselines, with 28.4% reduction in joint position error and 91.2% knowledge consistency score. Ablation studies confirm that knowledge graph integration is critical for generating culturally authentic movements. This research contributes a novel methodology for intangible cultural heritage preservation that captures both the physical execution and conceptual foundations of traditional martial arts.