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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 просмотров • 7 месяцев назад •via X (Twitter)

Наука и технологии Образование

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Комментарии: 29

Фото профиля Haian Jin

Haian Jin7 месяцев назад

🧵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).

Фото профиля Haian Jin

Haian Jin7 месяцев назад

🧵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).

Фото профиля Haian Jin

Haian Jin7 месяцев назад

🧵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.

Фото профиля Haian Jin

Haian Jin7 месяцев назад

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_.

Фото профиля ZotGuard

ZotGuard7 месяцев назад

VERY COOL!

Фото профиля Mayank Bhaskar

Mayank Bhaskar7 месяцев назад

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:

Фото профиля dailypapers.io

dailypapers.io7 месяцев назад

this is great work

Фото профиля Aryan Pandit ✈️ ECCV '26 🇸🇪

Aryan Pandit ✈️ ECCV '26 🇸🇪7 месяцев назад

Great work, seems really interesting!

Фото профиля Ang Cao

Ang Cao7 месяцев назад

Very cool work

Фото профиля Haian Jin

Haian Jin7 месяцев назад

Thank you, Ang!

Фото профиля ζ Pedram ζ

ζ Pedram ζ7 месяцев назад

Woah

Фото профиля Alexandre Devaux

Alexandre Devaux7 месяцев назад

Brilliant work! Looking forward to trying the code 🔥

Фото профиля 🎶

🎶7 месяцев назад

Cool

Фото профиля Yutang Lin

Yutang Lin7 месяцев назад

Great work, congrats!

Фото профиля Haian Jin

Haian Jin7 месяцев назад

Thank you, Yutang!

Фото профиля Wally

Wally7 месяцев назад

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!

Фото профиля 实践哥 Li

实践哥 Li7 месяцев назад

I love the zip idea

Фото профиля Brad Dangerfield

Brad Dangerfield7 месяцев назад

Sweet!

Фото профиля Utkarsh Mishra

Utkarsh Mishra7 месяцев назад

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

Фото профиля Sam Abdi

Sam Abdi7 месяцев назад

Seems really interesting and promising. Good work.

Фото профиля Haian Jin

Haian Jin7 месяцев назад

Thank you, Sam!

Фото профиля Daniel Sungho Jung

Daniel Sungho Jung7 месяцев назад

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?

Фото профиля Haian Jin

Haian Jin7 месяцев назад

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

Фото профиля Daniel Sungho Jung

Daniel Sungho Jung7 месяцев назад

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

Фото профиля Nigel Higgs

Nigel Higgs7 месяцев назад

Hit me with an update when I can use the code

Фото профиля Haian Jin

Haian Jin7 месяцев назад

They will be released soon!

Фото профиля Nigel Higgs

Nigel Higgs7 месяцев назад

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

Фото профиля Artem Sawistki

Artem Sawistki7 месяцев назад

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

Фото профиля MistFaccoco

MistFaccoco7 месяцев назад

nice work!

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76,380 просмотров • 3 лет назад

(1/N) Will this be the BERT/GPT moment for 3D vision？ Finally, unsupervised pre-training for 3D works. Led by Qitao Zhao , we present E-RayZer — a fully self-supervised 3D reconstruction model that: 🔥Matches or surpasses supervised methods like VGGT 👀Learns transferable 3D representations, outperforming CroCo, VideoMAE, and DINO 📈Scales with more unlabeled data A new recipe for scalable 3D foundation models.

(1/N) Will this be the BERT/GPT moment for 3D vision？ Finally, unsupervised pre-training for 3D works. Led by Qitao Zhao , we present E-RayZer — a fully self-supervised 3D reconstruction model that: 🔥Matches or surpasses supervised methods like VGGT 👀Learns transferable 3D representations, outperforming CroCo, VideoMAE, and DINO 📈Scales with more unlabeled data A new recipe for scalable 3D foundation models.

Hanwen Jiang

58,767 просмотров • 10 месяцев назад

This is SO crazy - The new "Spatial Scenes" feature turns your photos into frozen moments in time — making Vision Pro feel more like a real-life time machine than ever before. You can quite literally step into these photos, since they’re not just 3D versions but actually fully spatial. That means you can now look behind objects and change your perspective. If this isn’t pure sci-fi or magic come to life, I don’t know what is. I can’t stop smiling — we’re living in the craziest of all timelines.

This is SO crazy - The new "Spatial Scenes" feature turns your photos into frozen moments in time — making Vision Pro feel more like a real-life time machine than ever before. You can quite literally step into these photos, since they’re not just 3D versions but actually fully spatial. That means you can now look behind objects and change your perspective. If this isn’t pure sci-fi or magic come to life, I don’t know what is. I can’t stop smiling — we’re living in the craziest of all timelines.

Phil Traut ᯅ

89,103 просмотров • 1 год назад