Title: MaterialFusion: High-Quality, Zero-Shot, and Controllable Material Transfer with Diffusion Models
Primary area: generative models
Keywords: Diffusion models, Material transfer, Image editing

Abstract: Manipulating the material appearance of objects in images is critical for applications like augmented reality, virtual prototyping, and digital content creation. We present MaterialFusion, a novel framework for high-quality material transfer that allows users to adjust the degree of material application, achieving an optimal balance between new material properties and the object's original features. MaterialFusion seamlessly integrates the modified object into the scene by maintaining background consistency and mitigating boundary artifacts. To thoroughly evaluate our approach, we have compiled a dataset of real-world material transfer examples and conducted complex comparative analyses. Through comprehensive quantitative evaluations and user studies, we demonstrate that MaterialFusion significantly outperforms existing methods in terms of quality, user control, and background preservation.

TL;DR: MaterialFusion is a novel framework for high-quality material transfer that allows precise user control over material application while ensuring background consistency and reducing artifacts, significantly outperforming existing methods.