CoatFusion: Controllable Material Coating in Images
This addresses a novel image editing challenge for graphics and vision applications, offering more precise control over material appearance without overwriting details.
The paper tackles the problem of applying a thin material layer onto objects in images while preserving geometry, introducing a new task called Material Coating, and shows that CoatFusion outperforms existing methods with realistic, controllable results.
We introduce Material Coating, a novel image editing task that simulates applying a thin material layer onto an object while preserving its underlying coarse and fine geometry. Material coating is fundamentally different from existing "material transfer" methods, which are designed to replace an object's intrinsic material, often overwriting fine details. To address this new task, we construct a large-scale synthetic dataset (110K images) of 3D objects with varied, physically-based coatings, named DataCoat110K. We then propose CoatFusion, a novel architecture that enables this task by conditioning a diffusion model on both a 2D albedo texture and granular, PBR-style parametric controls, including roughness, metalness, transmission, and a key thickness parameter. Experiments and user studies show CoatFusion produces realistic, controllable coatings and significantly outperforms existing material editing and transfer methods on this new task.