Research Paper Analysis
Research on Dynamic Video of Qijiang Woodblock Prints Based on AIGC Technology
Authors: Qiang Lei, Yao Lu* (Corresponding), Qian Li, Linzhi Li, Ye Wang, Xin Qiu
Abstract: As a unique folk art form in Southwest China, Qijiang New Year pictures are facing the dual challenges of static communication limitation and insufficient technical adaptation in the digital transformation. Based on the technology of Generative Artificial Intelligence (AIGC), by constructing a multi-objective loss function that integrates the semantic alignment of text, color constraint and style feature preservation mechanism, the key problems such as color deviation and motion mutation in the dynamic process of traditional woodcut are solved. Combining optical flow-oriented interpolation algorithm and physical simulation post-processing technology, the optimization of time sequence consistency and the accurate expression of cultural symbols are realized. Through the double evaluation system of quantitative indicators and user cognitive experiments, the advantages of the algorithm in sports fluency, style fidelity and narrative consistency are verified. This technology can be applied to cultural heritage communication, educational tourism and video game industry, and its cross-domain application potential needs to be reconstructed by activating young people's immersive experience.
Unlocking Cultural Heritage with AI: Dynamic Qijiang Prints
This research pioneers an AIGC-driven approach to transform static Qijiang woodblock prints into dynamic videos, addressing limitations in traditional digital archiving and enhancing cultural engagement.
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Youth Coverage Increase
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Educational Consumption Increase
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Cultural IP Commercial Value
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Generation Cost Reduction
Deep Analysis & Enterprise Applications
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40%
Traditional blank rate in prints, often destroyed by current AIGC filling mechanisms.
23%
Decrease in cognitive efficiency due to over-generated visual elements (P < 0.05).
Enterprise Process Flow
Initial Scan
→
LAB Color Conversion
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Color Gamut Detection (∆E2000 > 5 for histogram matching; ∆E2000 ≤ 5 for Gaussian filtering)
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Multi-scale Gradient Extraction
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Binary Mask Generation
→
Culturally Authentic Data
61%
Increase in cultural symbol retention rate compared to Gatys method (CLIP score 0.82 vs 0.51).
Case Study: Multi-objective Loss Function for Style Fidelity
A multi-objective loss function (Ltotal = λ1LSD + λ2Lcolor + λ3Lstyle) integrates semantic alignment (λ1=1.0), color constraints (λ2=0.7), and style preservation (λ3=0.5) to address color deviation and motion mutation. This ensures accurate cultural symbol expression and aesthetic consistency.
| Algorithm | Key Feature | Qijiang Print Compatibility |
|---|---|---|
| Stable Diffusion | Western Art Focus, Automatic Filling |
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| Farnebäck Optical Flow | General Interpolation |
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| Proposed AIGC Framework | Multi-objective Loss, Optical Flow Optimization |
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Case Study: Qijiang Printmaking Challenge on TikTok
The dynamic video of 'Autumn Harvest' generated by AI achieved over 20M+ plays, with young user proportion increasing from 12% to 38%. Gestural interaction for grain scattering animation fostered 'watching-interacting-sharing' loop, boosting UGC content 17-fold.
73%
Improvement in overseas audience's cognitive accuracy of China folk customs.
| Field | Traditional Solution Issues | AI Solution Advantages | Performance Improvement Data |
|---|---|---|---|
| Cultural Heritage | Static display interaction rate < 5% | Gesture-triggered UGC interaction rate 32% | Youth coverage ↑ 216% |
| Education & Tourism | Knowledge retention rate < 30% | AR/VR immersive experience retention rate 65% | Educational consumption ↑ 180% |
| Film & Games | Hand-drawn scene cost $25/second | AI generation cost $1.2/second | Cultural IP commercial value ↑ 10x |
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