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Material Translation Based on Neural Style Transfer with Ideal Style Image Retrieval.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Neural Style Transfer (NST) enables transforming images based on style.
  • Material translation in NST relies heavily on the quality of reference images.

Purpose of the Study:

  • To develop an NST method for material translation using automatic style image retrieval.
  • To enhance the quality and realism of synthesized material translations.

Main Methods:

  • Proposed a Convolutional Neural Network (CNN)-feature-based image retrieval system.
  • Refined style image search by selecting discriminative images and preserving object semantics.
  • Integrated real-time material segmentation with NST for targeted material transfer.

Main Results:

  • The proposed method successfully translates materials like stone, wood, and metal.
  • Synthesized images were perceived as realistic, with some participants preferring them over real photographs.
  • Evaluated against state-of-the-art NST methods, demonstrating competitive or superior performance.

Conclusions:

  • Automatic style image retrieval significantly improves material translation in NST.
  • The method offers a robust approach for generating realistic material appearances.
  • Human perceptual studies validate the effectiveness and perceived realism of the synthesized results.