I work on fundamental algorithms for geometric and visual computing. Here's a taste of our group's work, as a list of "explainer" threads posted on Twitter/X over the years. (🧵)

For a whole lot more, check out:
cs.cmu.edu/~kmcrane/
geometry.cs.cmu.edu

If anyone's interested in what I've been up to lately, I've been leading development of some 3D foundation models at @Roblox—like a next-gen texture generator, released today: devforum.roblox.com/t/beta-introdu…

4k resolution, PBR materials… and it's free. :-)

Also gets some things right ractice (and annoying with other tools), like generating good texture even in spots where different parts of the mesh intersect.

Was happy this required not just ML, but some classic signal processing to really get right. Great work, by a great team.

Has definitely been a journey of understanding how generative AI tools can be designed as just another level of abstraction for creation—rather than a “one shot” replacement for artists. Still a lot of work to do, and more to come…

Amazing what you can get with just a single prompt with #Argon:

With everyone busily using LLMs + Blender/Three.js to make 3D models, I thought I'd try a different geometric representation.

This dragon isn't a mesh, NeRF, 3DGS, or video model: it's 27kb of GLSL code (generated via LLM), defining a closed-form implicit surface. 🧵 [1/n]

@iquilezles Forgot to share this diagram (generated from the code), which gives a sense of how the model is broken down into different levels of detail, per my request in the initial prompt.

Coarse-to-fine strategies are crucial for all sorts of spatial problems. Likely helps VLMs as well.

@iquilezles Another omission:

Even my reference image was a low-key cheat: I had good intuition that rounder shapes and simpler materials might reasonably be modeled via closed-form implicits.

A more realistic dragon would not have worked. (Yet diffusion-based mesh still models nail it…)