SniperJake945

@tearsofjake.bsky.social

Computer Graphics Investigator (CGI)

VoroNERF is coming along better now that i fixed a huge bug with my Top K selection of sites! It's still not perfect of course, lots of bugs and stuff to fix. This is with 60k voronoi sites, using the 16 closest sites around any sampling position along the render rays.

voronoi based NERF training isn't going incredibly well... 😂20k iterations with 80k voronoi sites takes about 10 hours to train. And it still looks dog water. Definitely need more sites and some better approach to accelerating sampling....

voronerf outputground truth lego from the back

Voronoi implicit but this time in 3d. It's not fully NERF mode yet, as I'm not doing any kind of sampling along rays, this is just trying to learn volumetric data. 30,000 Voronoi sites. Probably not enough for the pighead, but it's just a fun first test.

I'm once again making neural implicits of my cats. This time we're back to the voronoi. Gabor noise style. We weight the result of the softmax at any site i by sin(F_i*(x_i-x_j)•u_i) where F_i is learned frequency and u_i is a learned anisotropy direction. I call it Gaboroni

cat neural implicitgaboronoi tesselation

Wanted to make a neural implicit that's just layers of anisotropic simplex noise. Turns out it works pretty well. With 24 layers of simplex noise each with a 96x96 texture of anisotropy data we can get this kind of result! It's not at all a good compression method but it's fun and cool :)

predicted catsground truth cats

I had the idea to incorporate anisotropy into the recent Spherical Voronoi paper. And when we apply their ideas to euclidean problems (not Spherical) the results are pretty great when anisotropy is used. This is 3000 anisotropic sites vs 3000 isotropic sites. Same learning rates for both

Learned anisotropic repLearned isotropic repAnisotropic voronoiIsotropic voronoi

Shockingly annoying to find the points of intersection between a plane and an AABB. Even when I know the plane passes through the center of the box. If anyone has a simple method for finding the exact distance to a plane clipped by a bounding box I'd be eternally grateful.

Computing the exact bijection of the optimal transport (OT) problem between very large point sets is completely untractable… In our SIGGRAPH Asia 2025 paper: “BSP-OT: Sparse transport plans between discrete measures in log-linear time” we get one with typically 1% of error in a few seconds on CPU!

One of my coworkers, @tearsofjake.bsky.social, has a talk at SIGGRAPH this year about this really cool steerable perlin noise technique that was used on Moana 2. He's just posted some handy reference implementations for Houdini, Unity, Godot, and Blender; check it out! github.com/jakericedesi...

GitHub - jakericedesigns/SteerablePerlinNoise: Implementations of "Steerable Perlin Noise" as presented at Siggraph 2025

Implementations of "Steerable Perlin Noise" as presented at Siggraph 2025 - jakericedesigns/SteerablePerlinNoise

github.com

How do I get the discover page to align more with my interests? I've tried following a bunch of people, but it still kinda just shows stuff based off whatever categories I clicked when I signed up...

So what is the tiniest possible fluid simulation? Can we get anything interesting from a single cell? For this we'll be using a standard MAC grid, which means we'll represent horizontal velocities (red) on the vertical edges and vertical velocities (green) on the horizontal edges of each cell.

Bild

Recordings for Graphics Programming Conference 2024 are now up. The lineup is packed with amazing talks ranging from rendering for games like Tiny Glade, Hades, and Baldur’s Gate 3, to even a talk on designing your own GPU! youtube.com/playlist?lis...

Graphics Programming Conference 2024 - YouTube

The Graphics Programming Conference is a three day event in November. We have keynotes from industry experts, presentations, and “masterclass” workshops with...

youtube.com

Is there a robust and fast way of finding the nearest point on a paraboloid of form: (x,y, f(x) + f(y)) where f() could be any range of convex functions, like x^2 or x^4? So like given a point (p0,p1,p2) minimize: (x - p0)^2 + (y-p1)^2 + ((f(x) + f(y)) - p2)^2 With respect to x and y.