Poster
Alchemist: Parametric Control of Material Properties with Diffusion Models
Prafull Sharma · Varun Jampani · Yuanzhen Li · Xuhui Jia · Dmitry Lagun · Fredo Durand · William Freeman · Mark Matthews
Arch 4A-E Poster #116
[
Abstract
]
[ Project Page ]
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Oral
presentation:
Orals 6B Image & Video Synthesis
Fri 21 Jun 1 p.m. PDT — 2:30 p.m. PDT
[
Poster]
Fri 21 Jun 5 p.m. PDT
— 6:30 p.m. PDT
Fri 21 Jun 1 p.m. PDT — 2:30 p.m. PDT
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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