MaterialFusion: High-Quality, Zero-Shot, and Controllable Material Transfer with Diffusion Models
- URL: http://arxiv.org/abs/2502.06606v2
- Date: Wed, 12 Feb 2025 08:49:38 GMT
- Title: MaterialFusion: High-Quality, Zero-Shot, and Controllable Material Transfer with Diffusion Models
- Authors: Kamil Garifullin, Maxim Nikolaev, Andrey Kuznetsov, Aibek Alanov,
- Abstract summary: We present MaterialFusion, a novel framework for high-quality material transfer.
It allows users to adjust the degree of material application, achieving an optimal balance between new material properties and the object's original features.
- Score: 1.7749342709605145
- License:
- 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. Code is available at https://github.com/ControlGenAI/MaterialFusion.
Related papers
- Materialist: Physically Based Editing Using Single-Image Inverse Rendering [50.39048790589746]
We present a method combining a learning-based approach with progressive differentiable rendering.
Our method achieves more realistic light material interactions, accurate shadows, and global illumination.
We also propose a method for material transparency editing that operates effectively without requiring full scene geometry.
arXiv Detail & Related papers (2025-01-07T11:52:01Z) - IDArb: Intrinsic Decomposition for Arbitrary Number of Input Views and Illuminations [64.07859467542664]
Capturing geometric and material information from images remains a fundamental challenge in computer vision and graphics.
Traditional optimization-based methods often require hours of computational time to reconstruct geometry, material properties, and environmental lighting from dense multi-view inputs.
We introduce IDArb, a diffusion-based model designed to perform intrinsic decomposition on an arbitrary number of images under varying illuminations.
arXiv Detail & Related papers (2024-12-16T18:52:56Z) - MaterialFusion: Enhancing Inverse Rendering with Material Diffusion Priors [67.74705555889336]
We introduce MaterialFusion, an enhanced conventional 3D inverse rendering pipeline that incorporates a 2D prior on texture and material properties.
We present StableMaterial, a 2D diffusion model prior that refines multi-lit data to estimate the most likely albedo and material from given input appearances.
We validate MaterialFusion's relighting performance on 4 datasets of synthetic and real objects under diverse illumination conditions.
arXiv Detail & Related papers (2024-09-23T17:59:06Z) - IntrinsicAnything: Learning Diffusion Priors for Inverse Rendering Under Unknown Illumination [37.96484120807323]
This paper aims to recover object materials from posed images captured under an unknown static lighting condition.
We learn the material prior with a generative model for regularizing the optimization process.
Experiments on real-world and synthetic datasets demonstrate that our approach achieves state-of-the-art performance on material recovery.
arXiv Detail & Related papers (2024-04-17T17:45:08Z) - MatSynth: A Modern PBR Materials Dataset [4.548755617115688]
Mat Synth is a dataset of 4,000+ CC0 ultra-high resolution PBR materials.
Mat Synth is released through the project page at: https://www.gvecchio.com/matsynth.
arXiv Detail & Related papers (2024-01-11T17:20:34Z) - Intrinsic Image Diffusion for Indoor Single-view Material Estimation [55.276815106443976]
We present Intrinsic Image Diffusion, a generative model for appearance decomposition of indoor scenes.
Given a single input view, we sample multiple possible material explanations represented as albedo, roughness, and metallic maps.
Our method produces significantly sharper, more consistent, and more detailed materials, outperforming state-of-the-art methods by $1.5dB$ on PSNR and by $45%$ better FID score on albedo prediction.
arXiv Detail & Related papers (2023-12-19T15:56:19Z) - Alchemist: Parametric Control of Material Properties with Diffusion
Models [51.63031820280475]
Our method capitalizes on the generative prior of text-to-image models known for photorealism.
We show the potential application of our model to material edited NeRFs.
arXiv Detail & Related papers (2023-12-05T18:58:26Z) - MatFuse: Controllable Material Generation with Diffusion Models [10.993516790237503]
MatFuse is a unified approach that harnesses the generative power of diffusion models for creation and editing of 3D materials.
Our method integrates multiple sources of conditioning, including color palettes, sketches, text, and pictures, enhancing creative possibilities.
We demonstrate the effectiveness of MatFuse under multiple conditioning settings and explore the potential of material editing.
arXiv Detail & Related papers (2023-08-22T12:54:48Z) - Taming Encoder for Zero Fine-tuning Image Customization with
Text-to-Image Diffusion Models [55.04969603431266]
This paper proposes a method for generating images of customized objects specified by users.
The method is based on a general framework that bypasses the lengthy optimization required by previous approaches.
We demonstrate through experiments that our proposed method is able to synthesize images with compelling output quality, appearance diversity, and object fidelity.
arXiv Detail & Related papers (2023-04-05T17:59:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.