Summary of A Framework For Portrait Stylization with Skin-tone Awareness and Nudity Identification, by Seungkwon Kim et al.
A Framework for Portrait Stylization with Skin-Tone Awareness and Nudity Identification
by Seungkwon Kim, Sangyeon Kim, Seung-Hun Nam
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper presents a novel portrait stylization framework that addresses the challenges of preserving inherent characteristics while filtering out explicit content and maintaining quality. Building upon Stable Diffusion (SD), the proposed framework incorporates a nudity content identification module (NCIM) and a skin-tone-aware portrait stylization module (STAPSM). NCIM demonstrates good performance in enhancing explicit content filtering, while STAPSM accurately represents diverse skin tones. The framework has been successfully deployed in practice, meeting critical requirements for real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine taking a picture of someone and making it look like an old masterpiece painting! This paper is about creating a special tool that can do just that. But it’s not easy – the computer needs to make sure the new picture doesn’t show anything inappropriate, and it also needs to keep the person’s skin tone looking natural. The researchers created a new way to do this using something called Stable Diffusion. They tested their tool and found that it worked well at keeping explicit content out while making the pictures look great. |
Keywords
* Artificial intelligence * Diffusion
