Summary of Giving a Hand to Diffusion Models: a Two-stage Approach to Improving Conditional Human Image Generation, by Anton Pelykh et al.
Giving a Hand to Diffusion Models: a Two-Stage Approach to Improving Conditional Human Image Generation
by Anton Pelykh, Ozge Mercanoglu Sincan, Richard Bowden
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 This paper proposes a novel approach to pose-conditioned human image generation, addressing limitations in existing diffusion models. The method is divided into two stages: hand generation and body outpainting around the hands. A multi-task trained hand generator produces both hand images and segmentation masks, which are then used to train an adapted ControlNet model for outpainting. A novel blending technique ensures seamless fusion of the results from both stages. Experimental evaluations demonstrate the superiority of this approach over state-of-the-art techniques in terms of pose accuracy and image quality on the HaGRID dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates new ways to make fake human images, focusing on hands specifically. It’s like making a hand appear from nothing! The method has two steps: first, it makes a hand, then it adds the rest of the body around it. To do this, it uses special training that teaches the computer to generate both hand pictures and maps showing where the hand is. Then, it takes those maps and uses them to add the body. This paper shows its approach works better than other methods at making hands look right and having control over how they’re positioned. |
Keywords
* Artificial intelligence * Image generation * Multi task
