Summary of Multi-modal and Multi-scale Spatial Environment Understanding For Immersive Visual Text-to-speech, by Rui Liu and Shuwei He and Yifan Hu and Haizhou Li
Multi-modal and Multi-scale Spatial Environment Understanding for Immersive Visual Text-to-Speech
by Rui Liu, Shuwei He, Yifan Hu, Haizhou Li
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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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 M2SE-VTTS model aims to generate immersive reverberant speech from environmental images, considering both local and global spatial information. To achieve this, the multi-modal and multi-scale scheme integrates RGB and Depth image patches with Gemini-generated environment captions for local spatial understanding. The model outperforms advanced baselines in objective and subjective evaluations, demonstrating its effectiveness in modeling interactions between local and global spatial contexts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Visual Text-to-Speech (VTTS) is a technology that turns pictures into speech. Researchers are trying to make this happen by using both what’s seen in the image (like colors and shapes) and what’s not seen (like depth). They want to create realistic, immersive audio that sounds like it’s coming from different places within the environment. To do this, they’re developing a new way of understanding spatial information from images, which involves combining color and depth data with captions about the scene. This approach has been shown to be better than previous methods in generating spoken content. |
Keywords
» Artificial intelligence » Gemini » Multi modal
