2025 WACV WACV 2025

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.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Deep Learning
🧭 Keyword Pioneer — surface pattern
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio