Summary of Towards Kinetic Manipulation Of the Latent Space, by Diego Porres
Towards Kinetic Manipulation of the Latent Space
by Diego Porres
First submitted to arxiv on: 15 Sep 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 introduces Visual-reactive Interpolation, a novel approach to manipulating the latent space of generative models. By leveraging pre-trained Convolutional Neural Networks (CNNs) and a live RGB camera feed, this method enables simple changes in the scene to induce vast improvements in the latent space. The authors demonstrate the effectiveness of their approach, highlighting the potential for exploration and discovery in previously unexplored regions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to manipulate the latent space of generative models using Visual-reactive Interpolation. This new paradigm uses pre-trained CNNs and a live RGB camera feed to make simple changes to the scene, which can greatly improve the latent space. The authors provide an open-source code repository for this method. |
Keywords
» Artificial intelligence » Latent space
