This study proposes an intelligent restoration and immersion system for digital intangible cultural heritage, addressing the lack of multimodal data coordination. It uses a Multimodal Graph Convolutional Network (MM-GCN) to fuse images, craft texts, and dynamic action data, with spatiotemporal alignment and adaptive weight allocation to optimize modal contributions. The dual-channel architecture integrates visual restoration via MS-GAN with semantic constraints and tactile feedback through a mechanical model, achieving 4K/60fps real-time rendering. The system achieves an average structural similarity of 0.915, a feature extraction rate of 44.2fps, and a tactile feedback delay under 0.85ms. This algorithm provides a high-precision, low-latency solution for the digitization of intangible cultural heritage, promoting the living inheritance of cultural skills.
YiChen Du (Sun,) studied this question.
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