Summary of Raven: Rethinking Adversarial Video Generation with Efficient Tri-plane Networks, by Partha Ghosh et al.
RAVEN: Rethinking Adversarial Video Generation with Efficient Tri-plane Networks
by Partha Ghosh, Soubhik Sanyal, Cordelia Schmid, Bernhard Schölkopf
First submitted to arxiv on: 11 Jan 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 unconditional video generative model addresses long-term spatial and temporal dependencies while prioritizing computational and dataset efficiency. The hybrid explicit-implicit tri-plane representation, inspired by 3D-aware generative frameworks, employs a single latent code to model an entire video clip. This approach reduces the computational complexity compared to state-of-the-art methods, allowing for efficient and temporally coherent generation of videos. Additionally, the joint frame modeling mitigates visual artifacts, while an optical flow-based module integrated into the GAN-based generator enhances the model’s capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make fake videos is invented! This system can create realistic videos that are very long and have lots of details. It does this by using a special way to store information about the video, which makes it faster and better than other systems. This means we can make longer and more detailed fake videos that look real! |
Keywords
* Artificial intelligence * Gan * Generative model * Optical flow
