Related papers: CoatFusion: Controllable Material Coating in Images

CoatFusion: Controllable Material Coating in Images

URL: http://arxiv.org/abs/2512.02143v1
Date: Mon, 01 Dec 2025 19:13:30 GMT
Title: CoatFusion: Controllable Material Coating in Images
Authors: Sagie Levy, Elad Aharoni, Matan Levy, Ariel Shamir, Dani Lischinski,
Abstract summary: We introduce Material Coating, a novel image editing task that simulates applying a thin material layer onto an object.<n>CoatFusion produces realistic, controllable coatings and significantly outperforms existing material editing and transfer methods on this new task.
Score: 32.84440595085109
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: 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.

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