Harmonizing Attention: Training-free Texture-aware Geometry Transfer

Eito Ikuta, Yohan Lee, Akihiro Iohara, Yu Saito, Toshiyuki Tanaka

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.