Summary of Generative Zoo, by Tomasz Niewiadomski and Anastasios Yiannakidis and Hanz Cuevas-velasquez and Soubhik Sanyal and Michael J. Black and Silvia Zuffi and Peter Kulits
Generative Zoo
by Tomasz Niewiadomski, Anastasios Yiannakidis, Hanz Cuevas-Velasquez, Soubhik Sanyal, Michael J. Black, Silvia Zuffi, Peter Kulits
First submitted to arxiv on: 11 Dec 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 The proposed model-based estimation of 3D animal pose and shape from images enables computational modeling of animal behavior. To train models for this purpose, large amounts of labeled image data with precise pose and shape annotations are required. However, capturing such data is impractical to adapt to wild animals in situ and impossible to scale across a comprehensive set of animal species. The paper proposes an alternative approach to synthetic-data generation: rendering with a conditional image-generation model. A pipeline is introduced that samples diverse poses and shapes for various mammalian quadrupeds and generates realistic images with corresponding ground-truth pose and shape parameters. The GenZoo dataset, containing one million images of distinct subjects, is used to demonstrate the scalability of this approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about creating computer models of animals’ movements and body shapes from pictures. This helps scientists study animal behavior. To do this, they need lots of labeled pictures with exact information on what the animals are doing and how their bodies look. But it’s hard to get these pictures in real-life situations or for many different types of animals. Some people try to solve this problem by labeling pictures one by one, which takes a lot of time. Others create fake data using video games and 3D models, but this is also time-consuming. The paper proposes a new way to make fake pictures that are realistic and can be used to train computers to recognize animal movements and body shapes. |
Keywords
» Artificial intelligence » Image generation » Synthetic data
