Harmonizing Attention: Training-free Texture-aware Geometry Transfer

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 pho-tographs 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.

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Publication Details

Published
2025-02-26
DOI
https://doi.org/10.1109/wacv61041.2025.00205
Primary Topic
3D Shape Modeling and Analysis
Type
article
Field-Weighted Citation Impact
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article

Harmonizing Attention: Training-free Texture-aware Geometry Transfer

Toshiyuki Tanaka, Y. Saito, Akihiro Iohara, Yohan Lee et al.
3D Shape Modeling and Analysis
article

Harmonizing Attention: Training-free Texture-aware Geometry Transfer

Toshiyuki Tanaka, Y. Saito, Akihiro Iohara, Yohan Lee, Eito Ikuta
article en

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 pho-tographs 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.

Kyoto University (JP)
Openalex Percentile: Top 92%
3D Shape Modeling and Analysis
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