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Spatial reconstruction is a long-context problem: real scenes come with hundreds of images. But O(N²) transformer-based models don’t scale efficiently. Introducing: 🤐ZipMap (CVPR ’26): Linear-Time, Stateful 3D Reconstruction via Test-Time Training (TTT). ZipMap “zips” a large image collection into an implicit TTT scene state in a single linear-time operation.... show more

Haian Jin

1,258 subscribers

80,294 views • 8 months ago •via X (Twitter)

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29 Comments

Haian Jin8 months ago

🧵2/N Key to the speedup: we replace all global attention with TTT layers. Concretely, we first “zip” visual tokens into fast-weight MLPs by updating the weights with all tokens, then applying the updated MLPs back to the tokens. This update-then-apply mechanism plays a role analogous to vanilla bidirectional self-attention, while reducing the computational cost from O(N²) to O(N).

Haian Jin8 months ago

🧵3/N Fast speed without sacrificing quality: With careful designs of TTT, our model shows strong results across multiple benchmarks for both short and long sequence input, while being significantly faster. Left: Our runtime scales linearly; Given 750 images: <10s (ours) vs >200s (VGGT). Right: Our accuracy matches or surpasses quadratic-time SOTA (e.g., VGGT, π³).

Haian Jin8 months ago

🧵4/N Stateful reconstruction: the updated TTT fast weight can be viewed as an implicit scene state. In our paper, we demonstrate the benefits of having a stateful representation in real-time scene state querying and its extension to sequential streaming reconstruction.

Haian Jin8 months ago

For more results and technical details, please visit our project page: This work was done while I was a student researcher at @GoogleDeepMind. I’m very grateful for the inspiring research environment and the invaluable support from my collaborators @ChrisWu6080, @tianyuanzhang99, @RuiqiGao, @jon_barron, @Jimantha, and @holynski_.

ZotGuard8 months ago

VERY COOL!

Mayank Bhaskar8 months ago

Congratulations on the @CVPR 2026 acceptance! Would you or any of your co-authors like to present your papers in the @Cohere_Labs Computer Vision community? Cohere Labs Community Page: Here's the playlist to previous talks, if you are interested:

dailypapers.io8 months ago

this is great work

Aryan Pandit ✈️ ECCV '26 🇸🇪8 months ago

Great work, seems really interesting!

Ang Cao8 months ago

Very cool work

Haian Jin8 months ago

Thank you, Ang!

ζ Pedram ζ8 months ago

Woah

Alexandre Devaux8 months ago

Brilliant work! Looking forward to trying the code 🔥

🎶8 months ago

Cool

Yutang Lin8 months ago

Great work, congrats!

Haian Jin8 months ago

Thank you, Yutang!

Wally8 months ago

Very cool! Been exploring AI-generated impostor pipelines for real-time character rendering and the main pain point is view interpolation artifacts. This looks it could sidestep that entirely, looking forward to play around with the model!

实践哥 Li8 months ago

I love the zip idea

Brad Dangerfield8 months ago

Sweet!

Utkarsh Mishra8 months ago

This is one of the most amazing things I've ever seen!

Sam Abdi8 months ago

Seems really interesting and promising. Good work.

Haian Jin8 months ago

Thank you, Sam!

Daniel Sungho Jung8 months ago

May i ask if there is any reason why robot and human motion were in main qualitative results? Any advantage of ZipMap in such scenarios?

Haian Jin8 months ago

Hi! We put them to just show how our methods work for dynamic scenes. We don’t mean to specifically emphasize it.

Daniel Sungho Jung7 months ago

Thanks for the reply! I was curious as I am doing the tasks. Looking forward to visiting your poster at CVPR2026 :)

Nigel Higgs8 months ago

Hit me with an update when I can use the code

Haian Jin8 months ago

They will be released soon!

Nigel Higgs8 months ago

Awesome 👏🏽 just curious how well do you think this would work on video game footage?

Artem Sawistki8 months ago

That's dope! Can't wait to see the code

MistFaccoco7 months ago

nice work!

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