Summary of Sat3d: Image-driven Semantic Attribute Transfer in 3d, by Zhijun Zhai et al.
SAT3D: Image-driven Semantic Attribute Transfer in 3D
by Zhijun Zhai, Zengmao Wang, Xiaoxiao Long, Kaixuan Zhou, Bo Du
First submitted to arxiv on: 3 Aug 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 The proposed Semantic Attribute Transfer method in 3D (SAT3D) aims to manipulate image attributes in the latent space of generative models. Unlike previous approaches that focus on editing attributes in images with ambiguous semantics or regions from a reference image, SAT3D achieves photographic semantic attribute transfer, such as transferring a beard from a photo of a man. The method learns correlations between semantic attributes and style code channels by exploring the style space of a pre-trained 3D-aware StyleGAN-based generator. A Quantitative Measurement Module (QMM) is developed to quantify attribute characteristics in images based on descriptor groups, leveraging CLIP’s image-text comprehension capability. During training, the QMM guides target semantic transferring and irrelevant semantics preserving by calculating attribute similarity between images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new method for editing image attributes. It uses a special kind of artificial intelligence called generative models to change how an image looks. For example, it can make someone in a picture have a beard or make an animal look like a dog. The method is good at doing this and can even copy the way people edit pictures by hand. |
Keywords
* Artificial intelligence * Latent space * Semantics
