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A painting art rendering system by deep learning framework and machine translation.

Suyimeng Wang1, Safrizal Shahir2, Muhammad Uzair Ismail1

  • 1School of the Arts, Universiti Sains Malaysia, 11800, Penang, Malaysia.

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|December 30, 2025
PubMed
Summary

This study introduces an AI system for ethnic painting education, enhancing technique transmission and cross-linguistic understanding. The deep learning system significantly improves student skills in mastery, cultural insight, and creativity.

Keywords:
Cultural heritageDeep learningEthnic universitiesMachine translationPainting art rendering

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Area of Science:

  • Educational Technology
  • Artificial Intelligence
  • Cultural Heritage Studies

Background:

  • Ethnic painting instruction faces challenges in technique transmission, cross-linguistic communication, and personalization.
  • Educational informatization and cultural heritage preservation require innovative solutions for art education.

Purpose of the Study:

  • To develop and validate a deep learning and machine translation-based system for ethnic painting art rendering and instruction.
  • To establish an integrated framework for technique transmission, style rendering, cultural interpretation, and personalized guidance in ethnic art education.

Main Methods:

  • An improved generative adversarial network was used for automatic rendering of eight ethnic painting styles.
  • A visual-context Transformer was employed for semantic mapping of painting terminology across ethnic languages.
  • A multimodal dataset of 12,000 artworks and 5,000 terminology entries was utilized for validation.

Main Results:

  • The style rendering module achieved a 92.3% F1 score, an 8.7% improvement over traditional methods.
  • The terminology mapping module reached an 89.6% semantic matching rate, a 6.2% increase.
  • Student performance improved by 18.4% in technique mastery, 25.4% in cultural understanding, and 17.6% in creative innovation.

Conclusions:

  • The proposed system offers a practical approach for digital preservation of ethnic painting techniques and cross-cultural communication in art education.
  • The collaborative framework and innovative modules significantly enhance educational value and student learning outcomes.
  • Deep learning and machine translation provide effective solutions for challenges in ethnic art instruction.