Summary of Annotated Hands For Generative Models, by Yue Yang and Atith N Gandhi and Greg Turk
Annotated Hands for Generative Models
by Yue Yang, Atith N Gandhi, Greg Turk
First submitted to arxiv on: 26 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 training framework for generative models, such as GANs and diffusion models, significantly improves their ability to generate high-quality images with hands. By augmenting the training images with additional annotations that provide structure to hands in the image, the model learns to produce more realistic hand images. The approach is demonstrated on two different generative models and tested on both synthetic and real-world datasets of hand images. The improved quality of generated hands is evaluated using an off-the-shelf hand detector. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has found a way to make computers better at drawing pictures with hands. They took existing computer programs that create images, like GANs and diffusion models, and gave them extra information about what makes a hand look real. This helps the program create more accurate and detailed images of hands. The scientists tested their new approach on two different types of image-making software and showed it works well with both synthetic and real-world pictures. |
Keywords
» Artificial intelligence » Diffusion
