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CoatFusion model enables controllable material coating in images

Researchers have introduced CoatFusion, a new diffusion model architecture designed for a novel image editing task called Material Coating. This task involves applying a thin layer of material onto an object in an image while preserving its geometry, distinguishing it from traditional material transfer methods. To support this, a large synthetic dataset named DataCoat110K, comprising 110,000 images with physically-based coatings, was created. Experiments demonstrate that CoatFusion outperforms existing methods in generating realistic and controllable coatings. AI

IMPACT Introduces a new image editing capability for applying realistic and controllable material coatings.

RANK_REASON Academic paper detailing a new model and dataset for image editing. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

CoatFusion model enables controllable material coating in images

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Academic paper detailing a new model and dataset for image editing. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Sagie Levy, Elad Aharoni, Matan Levy, Ariel Shamir, Dani Lischinski ·

    CoatFusion: Controllable Material Coating in Images

    arXiv:2512.02143v2 Announce Type: replace-cross 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 f…