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Can neural fields help unlock new understandings of an old geometry problem? Excited to announce our latest SIGGRAPH Asia work: Variational Barycentric Coordinates! 🧵
- with Oded Stein, @vsitzmann, Justin Solomon
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Charles Babbage intentionally designed the computer as an automation of mental labor. Today, computer graphics automates visual art production via privately governed generative models. How did we get here?
Critical Labor History of Generative Models, SIGGRAPH Talks '26 🧵
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Our neural fields satisfy the barycentric constraints even with a randomly initialized network!
The secret: simplex barycentric coordinates are easy ➡️ we decompose the shape into “virtual simplices” and learn how to combine those.
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🔥 Top post: Can neural fields help unlock new understandings of an old geome · 28 likes + reposts
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Can neural fields help unlock new understandings of an old geometry problem? Excited to announce our latest SIGGRAPH Asia work: Variational Barycentric Coordinates! 🧵
- with Oded Stein, @vsitzmann, Justin Solomon
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Charles Babbage intentionally designed the computer as an automation of mental labor. Today, computer graphics automates visual art production via privately governed generative models. How did we get here?
Critical Labor History of Generat…
"Attention is just XYZ". Attention is actually a Rorschach test for researchers.
Arrived to beautiful Saint Malo for the Curves and Surfaces conference! Let me know if you wanna chat about curves OR surfaces. Or heck, even volumes, why not, let's get nutty with it!
Here is a link to the paper, and see you at SIGGRAPH Asia!
https://anadodik.github.io/publication/vbc/vbc.pdf
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Flexibility! We can now use any loss we want, including deformation-aware ones.
Instead of us PDE nerds arguing over which smoothness energy works best for cage-based deformations, artists can now choose to e.g. make a cage-based deformati…
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Besides being able to produce cute Australia-themed animations, we prove that our neural fields represent the _entire_ function class of barycentric coordinates. In other words, we made a general barycentric coordinate function approximato…
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Our neural fields satisfy the barycentric constraints even with a randomly initialized network!
The secret: simplex barycentric coordinates are easy ➡️ we decompose the shape into “virtual simplices” and learn how to combine those.
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