Summary of Egogen: An Egocentric Synthetic Data Generator, by Gen Li et al.
EgoGen: An Egocentric Synthetic Data Generator
by Gen Li, Kaifeng Zhao, Siwei Zhang, Xiaozhong Lyu, Mihai Dusmanu, Yan Zhang, Marc Pollefeys, Siyu Tang
First submitted to arxiv on: 16 Jan 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 introduces EgoGen, a synthetic data generator that produces accurate and rich ground-truth training data for egocentric perception tasks in Augmented Reality (AR). The challenge lies in simulating natural human movements and behaviors to capture a faithful egocentric representation of the 3D world. EgoGen addresses this challenge by leveraging egocentric visual inputs of a virtual human to sense the environment, using collision-avoiding motion primitives and a two-stage reinforcement learning approach. This closed-loop solution eliminates the need for a pre-defined global path and is applicable to dynamic environments. The paper demonstrates EgoGen’s efficacy in three tasks: mapping and localization for head-mounted cameras, egocentric camera tracking, and human mesh recovery from egocentric views. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EgoGen is a new way to make virtual humans move like real people so that their cameras can capture the world from their perspective. This is important because it helps computers learn how to see the world in the same way we do. The problem was that making these virtual humans move realistically was hard, especially when they had to avoid bumping into things. To solve this, researchers created a new system called EgoGen that uses visual inputs and special movements to help the virtual human navigate. This system is really good at helping computers learn how to see the world from our perspective. |
Keywords
* Artificial intelligence * Reinforcement learning * Synthetic data * Tracking
