Harmonizing Attention: Training-Free Texture-Aware Geometry Transfer

Abstract

Creating images where surface patterns of one object - such as cracks holes or grooves - are precisely transferred onto objects made of different materials remains a challenging task in computer graphics. For example recreating the exact pattern of wood grain cracks on a metallic surface while maintaining the realistic metallic texture requires sophisticated technical solutions. In this study we introduce Harmonizing Attention a new method that can automatically extract these surface patterns from photographs and recreate them with different materials while preserving natural-looking textures. Our approach achieves this through a novel attention mechanism that can process multiple reference images simultaneously without requiring additional training. This makes the method both practical and efficient for real-world applications opening up new possibilities in augmented reality image editing and beyond.

Cite

Text

Ikuta et al. "Harmonizing Attention: Training-Free Texture-Aware Geometry Transfer." Winter Conference on Applications of Computer Vision, 2025.

Markdown

[Ikuta et al. "Harmonizing Attention: Training-Free Texture-Aware Geometry Transfer." Winter Conference on Applications of Computer Vision, 2025.](https://mlanthology.org/wacv/2025/ikuta2025wacv-harmonizing/)

BibTeX

@inproceedings{ikuta2025wacv-harmonizing,
  title     = {{Harmonizing Attention: Training-Free Texture-Aware Geometry Transfer}},
  author    = {Ikuta, Eito and Lee, Yohan and Iohara, Akihiro and Saito, Yu and Tanaka, Toshiyuki},
  booktitle = {Winter Conference on Applications of Computer Vision},
  year      = {2025},
  pages     = {2042-2051},
  url       = {https://mlanthology.org/wacv/2025/ikuta2025wacv-harmonizing/}
}