apple

apple / ml-lito

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[ICLR 2026] LiTo: Surface Light Field Tokenizatio

apple.github.ioml-lito
19
2
100% credibility
Found Mar 16, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

Apple's research repository for LiTo, a technique that represents 3D object geometry and realistic view-dependent appearance using surface light field tokenization.

How It Works

1
🔍 Discover LiTo

You come across this Apple research project on GitHub that creates lifelike 3D objects with realistic lighting and reflections.

2
🌐 Visit the project page

Click over to the demo website to see impressive examples of 3D models that look just like the real thing from any angle.

3
✨ See the magic happen

Watch generated 3D scenes capture shiny surfaces, changing lights, and natural glows that make everything feel alive and real.

4
📖 Read the story behind it

Check out the research paper summary to understand how they blend shapes and lighting into one smart representation.

5
💡 Explore more details

Look at the provided samples and credits to appreciate the work that went into these stunning visuals.

🎉 Feel inspired

You've now seen the future of creating beautiful 3D worlds with AI that handles light and materials perfectly.

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Star Growth

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AI-Generated Review

What is ml-lito?

This GitHub repo hosts LiTo, a research project from Apple for ICLR 2026 on surface light field tokenization. It creates a unified 3D latent space from RGB-depth images that captures both object geometry and view-dependent effects like reflections under complex lighting. Users get a latent flow matching model to generate full 3D objects from a single input image, with consistent materials and lighting—check the project page for samples.

Why is it gaining traction?

Amid GitHub ICLR 2026 leak buzz on Reddit and OpenReview, it stands out by jointly modeling geometry and appearance, outperforming methods stuck on diffuse-only or geometry reconstruction. Developers notice sharper view synthesis and better latent distributions for generation tasks. The arXiv paper and ICLR 2026 citation format fuel early shares among ML circles.

Who should use this?

3D vision researchers prepping ICLR 2026 submissions or workshops, especially those in radiance fields and novel view synthesis. Teams at Apple-scale labs prototyping image-to-3D pipelines needing view-dependent realism.

Verdict

Promising ICLR 2026 paper idea with strong authors, but 1.0% credibility score and 19 stars reflect its raw state—just a README, licenses, and project page, no code yet. Watch for post-deadline releases; bookmark if surface light fields intrigue you. (178 words)

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