Summary of Lightit: Illumination Modeling and Control For Diffusion Models, by Peter Kocsis (1) et al.
LightIt: Illumination Modeling and Control for Diffusion Models
by Peter Kocsis, Julien Philip, Kalyan Sunkavalli, Matthias Nießner, Yannick Hold-Geoffroy
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary We introduce LightIt, a method for explicit illumination control in image generation, which lacks in recent generative models. This is crucial for artistic aspects like setting the mood or cinematic appearance. To overcome this limitation, we condition the generation on shading and normal maps using single bounce shading with cast shadows. We first train a shading estimation module to generate real-world images and shading pairs. Then, we train a control network using estimated shading and normals as input. Our method achieves high-quality image generation and lighting control in various scenes. Additionally, we use our generated dataset to train an identity-preserving relighting model conditioned on an image and target shading. LightIt is the first method that enables controllable, consistent lighting generation, performing on par with specialized relighting state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’ve created a new way to control how bright or dark something looks in images. This is important for making images look more realistic and movie-like. We do this by using special maps that show the shape of objects and how light should be shining on them. First, we train a machine learning model to create these maps from real-world pictures. Then, we use those maps to control the lighting in our generated images. Our new method can make high-quality images with the right amount of light or shadow. We’ve also used our technology to train another model that can change the lighting of an image without changing what’s in it. |
Keywords
* Artificial intelligence * Image generation * Machine learning
