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Lucky Iyinbor

@Luckyballa4,805 subscribers

Machine Learning | Physics Simulation | Geometry Processing | Computer Graphics | AR/VR Building things at @trymirai Thinking at @CopileHQ

Shorts

Found some time to add scene collisions to my Vision Pro playground, and I did not expect this level of accuracy!

Found some time to add scene collisions to my Vision Pro playground, and I did not expect this level of accuracy!

1,747,732 Aufrufe

Imagine if all computer graphics papers were published like this 🥹

Imagine if all computer graphics papers were published like this 🥹

57,593 Aufrufe

Getting closer to the multiview surface/volume reconstruction I had on my mind for quite some time A lot of engineering was needed to make it real-time, but that’s the only way I can experiment

Getting closer to the multiview surface/volume reconstruction I had on my mind for quite some time A lot of engineering was needed to make it real-time, but that’s the only way I can experiment

20,007 Aufrufe

Added very naive edges and some energy-based regularization that is aligned with the medial axis I guess I could have made an auto-rigging tool, maybe another time

Added very naive edges and some energy-based regularization that is aligned with the medial axis I guess I could have made an auto-rigging tool, maybe another time

13,530 Aufrufe

Videos

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The Genie 3 release is a perfect moment to have a discussion about the future of 3D But first it would be nice to make the terminology more clear, specifically: What is a “spatial representation” Implicit vs Explicit Generalization vs Specialization Reconstruction vs Generation Production vs Execution Let’s start: For me, a spatial representation is just a way to describe a thing in the physical world The core property that makes it useful is consistency You can enforce consistency explicitly via rendering equations, geometric constraints, and physics Or implicitly, purely through training data Then, your representation parameters can be explicit, like points, gaussians, triangles, voxels, etc. Or implicit, weights or latent vectors Parameters alone are not the representation. It’s a combination of the parameters, the process that produces them, and the way you materialize them through a function (physics-based rendering, simulation, neural network, etc.) Generalization means you take data from multiple scene observations, and then produce a map from desired input to representation parameters Specialization means you take single-scene observations and directly fit a function parameters to describe thar scene Many representations can serve both of the approaches, as long as you keep them differentiable Both of the above can be used for reconstruction, where the main goal is to explain observations through a lens of physics (hard constraint) On the other hand, generation needs generalization, and its task is to produce statistically plausible results that could be conditioned on observations (soft constraint) Both tasks are not solved yet and they can complement each other in various ways Yet another important aspect is the difference between production and execution Production = process of going from inputs to parameters Execution = process of going from parameters to result It’s important to separate these, because most usecases require fast execution to be viable which is severely constrained by the hardware So, are *world models* like Genie an important step forward? Yes Do they make other representations obsolete? Maybe some of them - but there are tons of economically valuable tasks that won’t be solved by it, at least in any observable future

Lucky Iyinbor

13,959 Aufrufe • vor 4 Monaten

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SDF Sphere Packing 🔮

Lucky Iyinbor

36,053 Aufrufe • vor 1 Jahr

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