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Depth-Aware Style Transfer.pdf

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Preview of Depth-Aware Style Transfer
🔗 Source: orca.cardiff.ac.uk
📊 Size: 22.14 MB
👤 Author: Xiao-Chang Liu, Ming-Ming Cheng, Yu-Kun Lai, and Paul L. Rosin
⬇️ Downloads: 180

Summary

Neural style transfer has received significant attention, but existing methods often produce unsatisfactory results when input images contain multiple objects at different depths, destroying image layout and obscuring boundaries. This paper introduces a novel approach that integrates depth preservation as an additional loss, preserving overall image layout while performing style transfer, and achieves better results than existing methods.

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Description

Neural style transfer has received significant attention, but existing methods often produce unsatisfactory results when input images contain multiple objects...

Technical Information

  • File Format: PDF
  • File Size: 22.14 MB
  • Pages: 10
  • Language: EN
  • Author: Xiao-Chang Liu, Ming-Ming Cheng, Yu-Kun Lai, and Paul L. Rosin
  • Total Downloads: 180
  • Last Updated: 10 hours ago

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This PDF document about Depth-Aware Style Transfer provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Depth-Aware Style Transfer.

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