Summary of Sketch-guided Image Inpainting with Partial Discrete Diffusion Process, by Nakul Sharma et al.
Sketch-guided Image Inpainting with Partial Discrete Diffusion Process
by Nakul Sharma, Aditay Tripathi, Anirban Chakraborty, Anand Mishra
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper introduces a novel partial discrete diffusion process (PDDP) for sketch-guided image inpainting, which allows users to specify the object’s shape and pose. The PDDP combines a forward pass that corrupts masked regions with a backward pass that reconstructs these regions conditioned on hand-drawn sketches using a proposed sketch-guided bi-directional transformer. The transformer module accepts an image containing masked regions and a query sketch, effectively addressing the domain gap between sketches and natural images. The paper evaluates the method against various competent approaches in the literature and establishes its effectiveness through qualitative and quantitative results as well as user studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to fill in missing parts of an image by using drawings. It’s like having more control over what gets filled in, and it uses a special kind of computer model that learns from examples. The researchers compared their method to others and showed that it works better for certain types of images. This could be useful for things like restoring old photos or creating new art. |
Keywords
» Artificial intelligence » Diffusion » Image inpainting » Transformer
