Summary of Bodymetric: Evaluating the Realism Of Human Bodies in Text-to-image Generation, by Nefeli Andreou et al.
BodyMetric: Evaluating the Realism of Human Bodies in Text-to-Image Generation
by Nefeli Andreou, Varsha Vivek, Ying Wang, Alex Vorobiov, Tiffany Deng, Raja Bala, Larry Davis, Betty Mohler Tesch
First submitted to arxiv on: 5 Dec 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 tackles a challenging problem in computer vision, accurately generating images of human bodies from text inputs. Current state-of-the-art text-to-image models often produce unrealistic or incorrect body parts, making it difficult to evaluate their performance at scale. To address this issue, the authors propose BodyMetric, a learnable metric that predicts body realism in images. This metric is trained on realism labels and multi-modal signals including 3D body representations and textual descriptions. The authors also design an annotation pipeline to collect expert ratings on human body realism, resulting in the BodyRealism dataset. The paper demonstrates the effectiveness of BodyMetric through applications such as benchmarking text-to-image models for generating realistic human bodies and ranking generated images based on predicted realism scores. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you want a computer to create an image of a person from some words. That’s a hard task! Right now, computers aren’t very good at it because they often add or remove body parts that don’t make sense. To solve this problem, the authors came up with a new way to measure how realistic these images are. They created a tool called BodyMetric that can predict how realistic an image is just by looking at it and some words about the person in the picture. This helps computers evaluate themselves better and create more realistic people in pictures. The authors also made a big dataset of pictures with labels so they could train their tool to get better. |
Keywords
» Artificial intelligence » Multi modal
