Papers › Towards Kinetic Manipulation of the Latent Space
Towards Kinetic Manipulation of the Latent Space
Diego Porres
The latent space of many generative models are rich in unexplored valleys and mountains. The majority of tools used for exploring them are so far limited to Graphical User Interfaces (GUIs). While specialized hardware can be used for this task, we show that a simple feature extraction of pre-trained Convolutional Neural Networks (CNNs) from a live RGB camera feed does a very good job at manipulating the latent space with simple changes in the scene, with vast room for improvement. We name this new paradigm Visual-reactive Interpolation, and the full code can be found at https://github.com/PDillis/stylegan3-fun.
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Code
Syntology Ran 14 of 18 code samples harvested from 1 repository linked to this paper; 4 have no recorded run. Of those that ran: 4 ran · honoured contract; 2 ran · our draft was wrong; 2 ran · fixture could not drive it; 6 ran with no contract checked.
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Code Syntology ran Syntology
18 samples harvested; 14 ran; 4 honoured the contract we drafted; 4 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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