| Autor: |
Sharma, Prafull, Jampani, Varun, Li, Yuanzhen, Jia, Xuhui, Lagun, Dmitry, Durand, Fredo, Freeman, William T., Matthews, Mark |
| Rok vydání: |
2023 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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