Built with img2threejs. No Blender. No GLB. Just native code you own.I spent a full day on it. Does this feel more satisfying?
Still kinda wild seeing this work.
No Blender. No GLB. No traditional modeling pipeline.
Just code you can inspect, tweak, rig, animate, and make your own
Instead of photogrammetry or mesh extraction, this reconstructs an object entirely in code. Every part is a procedural primitive with parameters derived from the reference, then refined pass-by-pass, comparing renders to the photo and fixing whatever's off.
The interesting constraint: no pass is allowed to "look good globally" if an identity-defining feature is wrong (e.g. the knife's serration count, the paracord weave). Each detail must map to an actual component/material override, not vibes.
Results + code below: knife is near-photoreal, others are deliberately stylized/low-poly.
Hey dudes,
I’m building img2threejs, a skill that turns a single image into a procedural Three.js 3D model (code only).
It currently works best for objects. With more context and detailed descriptions, the generated model gets much closer to the real one, but writing those prompts takes time.
Question: Is there a better way to preserve quality without requiring so much manual context?
https://reddit.com/link/1uyjnzt/video/azwpmwhliodh1/player
Another challenge is human models. They’re still much harder to generate accurately. If you know useful concepts, datasets, or techniques for procedural human modeling, I’d love to learn from you.
If you like the project, please leave any feedback or ideas in the comments. Check showcases out here: https://hoainho.github.io/img2threejs-showcase/
Open-source toolkit that rebuilds the object in a single reference image as procedural Three.js code (no mesh downloads), quality-gated by a render-vs-reference loop - strong for hard-surface objects
Check it out: https://github.com/hoainho/img2threejs
I've been experimenting with letting an AI agent debug/drive a React Native app, and wanted to share the technical approach + numbers, because the naive way turned out to be surprisingly bad.
The naive approach: vision loops. Most "AI drives your app" setups screenshot the screen every step and send the image to a vision model to decide the next tap. It works in demos but has two real problems:
What worked better: give the agent structured data, not pixels.
Element hierarchy instead of screenshots — on iOS via the accessibility tree, on Android via ui automator. Same 8-step flow dropped to ~3,100 tokens (~5× less) because you're sending compact JSON, not images.
Takeaway for RN devs: if you're wiring any kind of agent/automation onto an RN app, lean on the accessibility tree + CDP that RN already gives you before reaching for vision. It's cheaper and far less flaky.
Full disclosure: this came out of an open-source MCP server I built (MIT, not a product, nothing to sell) — happy to link it in a comment if that's allowed, but mainly I wanted to share the approach and hear how others are handling agent-driven RN testing. What's worked for you?
