Summary of Text-to-events: Synthetic Event Camera Streams From Conditional Text Input, by Joachim Ott et al.
Text-to-Events: Synthetic Event Camera Streams from Conditional Text Input
by Joachim Ott, Zuowen Wang, Shih-Chii Liu
First submitted to arxiv on: 5 Jun 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 The paper presents a novel approach for creating large labelled event camera datasets, which are crucial for developing deep network algorithms using event cameras. By leveraging text-to-X models, where X is one or multiple output modalities, including events, the authors propose a method that generates synthetic event frames directly from text prompts. The proposed model combines an autoencoder trained on an event camera dataset with a diffusion model architecture to produce smooth synthetic event streams of moving objects. The generated sequences are evaluated using a classifier trained on real data, achieving classification accuracies ranging from 42% to 92%, depending on the gesture group. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Event cameras have advantages for tasks requiring low-latency and sparse output responses. However, developing deep network algorithms using these cameras has been slow due to the lack of large labelled event camera datasets. This paper presents a method to create new labelled event datasets by using text-to-X models, which generate synthetic event frames directly from text prompts. The proposed model combines an autoencoder trained on diverse scene data with a diffusion model architecture to produce realistic event sequences of human gestures prompted by different text statements. |
Keywords
» Artificial intelligence » Autoencoder » Classification » Diffusion model
