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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