Alchemist: Parametric Control of Material Properties with Diffusion Models

• URL: http://arxiv.org/abs/2312.02970v1
• Date: Tue, 5 Dec 2023 18:58:26 GMT
• Title: Alchemist: Parametric Control of Material Properties with Diffusion Models
• Authors: Prafull Sharma, Varun Jampani, Yuanzhen Li, Xuhui Jia, Dmitry Lagun, Fredo Durand, William T. Freeman, Mark Matthews
• Abstract summary: 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.
• Score: 51.63031820280475
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a method to control material attributes of objects like roughness, metallic, albedo, and transparency in real images. Our method capitalizes on the generative prior of text-to-image models known for photorealism, employing a scalar value and instructions to alter low-level material properties. Addressing the lack of datasets with controlled material attributes, we generated an object-centric synthetic dataset with physically-based materials. Fine-tuning a modified pre-trained text-to-image model on this synthetic dataset enables us to edit material properties in real-world images while preserving all other attributes. We show the potential application of our model to material edited NeRFs.

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