Summary of Representative Feature Extraction During Diffusion Process For Sketch Extraction with One Example, by Kwan Yun et al.
Representative Feature Extraction During Diffusion Process for Sketch Extraction with One Example
by Kwan Yun, Youngseo Kim, Kwanggyoon Seo, Chang Wook Seo, Junyong Noh
First submitted to arxiv on: 9 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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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 proposed method, DiffSketch, is a novel approach to generating stylized sketches from images. It focuses on selecting representative features from deep features within a pretrained diffusion model and can be trained with one manual drawing. The method also includes an efficient sketch extraction mechanism that distills a trained generator into a streamlined extractor. By analyzing denoising diffusion features and integrating them with VAE features, DiffSketch produces high-quality sketches. Furthermore, the paper proposes a sampling scheme for training models using a conditional generative approach. Through comparisons, it is shown that distilled DiffSketch outperforms existing state-of-the-art sketch extraction methods and surpasses diffusion-based stylization methods in extracting sketches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DiffSketch is a new way to create cool drawings from pictures. It takes the good parts of deep learning features and uses them to make simple and detailed drawings. You only need one example drawing to teach it how to do this. The method also includes a clever way to simplify the process and make it faster. By combining different techniques, DiffSketch can create really nice sketches. The paper shows that DiffSketch is better than other methods for making sketches and even does well at changing pictures to look like drawings. |
Keywords
* Artificial intelligence * Deep learning * Diffusion * Diffusion model
