Summary of A Versatile Diffusion Transformer with Mixture Of Noise Levels For Audiovisual Generation, by Gwanghyun Kim et al.
A Versatile Diffusion Transformer with Mixture of Noise Levels for Audiovisual Generation
by Gwanghyun Kim, Alonso Martinez, Yu-Chuan Su, Brendan Jou, José Lezama, Agrim Gupta, Lijun Yu, Lu Jiang, Aren Jansen, Jacob Walker, Krishna Somandepalli
First submitted to arxiv on: 22 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Multimedia (cs.MM); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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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 audiovisual latent diffusion model is a novel approach to learning conditional distributions of various input-output combinations for audiovisual sequences. The model can be trained in a task-agnostic fashion using a transformer-based architecture and variable diffusion timesteps, which allows for flexibility in introducing noise levels across modalities and temporal dimensions. This enables the generation of temporally and perceptually consistent samples conditioned on the input, surpassing baselines in various cross-modal and multimodal interpolation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you can generate audio and video sequences that are as realistic as they are fascinating! This paper presents a new way to do just that using something called “diffusion models”. These models learn how to create new audiovisual sequences by understanding the patterns between different sounds and images. The big idea here is that we can train one model to generate lots of different things, like music videos or animations, without having to train separate models for each task. This makes it much more efficient and opens up all sorts of creative possibilities. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Transformer
