Digital Texture Generation Model for Cultural Imagery and Virtual-Real Consistency Assessment
Abstract
Traditional texture synthesis methods struggle to capture high-level semantics due to the abstract and context-dependent nature of cultural imagery, which lacks direct pixel correspondence. To address this challenge in the intelligent generation of digital cultural assets, this article proposes a method for evaluating the virtual-real consistency of a culturally conditioned texture diffusion model (CTDiff). A domain-customized image vector space is constructed to quantize abstract cultural concepts, and a cultural modulation–diffusion architecture is designed to enable semantic-guided texture synthesis via feature-level affine transformation. A differentiable cultural consistency loss is introduced to constrain generated results to conform with historical visual paradigms. Furthermore, to overcome the insensitivity of conventional metrics to cultural semantics, a multidimensional assessment system combining Fréchet Incidence Distance (FID), an image fidelity score, and a user perception score is established. Experiments on the self-built dataset EquusTex-1K show that the proposed method achieves an FID of 18.3, an image fidelity score of 0.61, and an average user score of 4.23—significantly outperforming existing baselines. This work provides a feasible technical pathway for the algorithmic representation and credible generation of cultural imagery.
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