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[R] Neural Volumes: Learning Dynamic Renderable Volumes from Images — Source Code Release

We published our work, Neural Volumes, at SIGGRAPH this year. The goal of the paper is to create an animate-able 3D model of objects and scenes from calibrated multi-view video. You can check out the video in that link for a visual explanation of how the method works and to see some of the results. The formulation of the method is basically an autoencoder with a fixed-function rendering technique that renders a volume into an image.

We’ve just put the source code on Github today. Since calibrated multi-view video is relatively hard to come by, we’ve including the dry ice dataset as well as a pretrained model (in the v0.1 release). I’m happy to answer any questions about this work or talk about neural rendering in general.

submitted by /u/stephenlombardi
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.