Geometry as Address: Routing Attention to Visual Memory for Long-Horizon Camera-Controlled Video Generation

Zesong Yang, Weikai Chen, Liyuan Cui, Lutao Jiang, Runze Zhang, Yingda Yin, Xiaoyang Huang, Kai Yan and 5 others

Zhejiang University · HKUST(GZ) · LIGHTSPEED

Published September 28, 2026 · on the feed September 29, 2026 · ▲ 3 · arXiv 2609.34722

VideoVision & multimodal3D, worlds & games Empty repo arXiv ↗Hugging Face ↗

The abstract

Long-horizon camera-controlled video generation requires recovering previously observed content from an ever-growing visual history. Existing approaches either search historical context implicitly or reconstruct it into persistent 3D memory, facing inefficient memory access or accumulated geometric errors. Our key insight is that geometry need not explain the scene--it only needs to determine where visual memory should be read from, while attention decides what should be recovered. Based on this insight, we introduce GEAR, a Geometry-Enabled Attention Routing framework that uses geometry as an explicit token-level address for visual memory. Rather than fusing historical observations into a persistent global 3D representation, GEAR retains them as frame latents and uses per-frame geometry only to establish token-level correspondences with target views, thereby avoiding persistent error accumulation from global fusion. Guided by these correspondences, Geometric Correspondence Attention (GCA) selectively injects geometrically matched historical features into noisy target patches during denoising. We further introduce an Invisible Octree to accumulate visibility evidence and reject geometrically plausible but occluded correspondences. Extensive experiments demonstrate that GEAR achieves state-of-the-art visual quality, precise camera control, and revisit consistency, enabling minute-long video generation along challenging trajectories.

The Brief

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A short podcast summary of the paper. Written and voiced by AI.

Transcript

Geometry as Address. Routing Attention to Visual Memory for Long-Horizon Camera-Controlled Video Generation. By Zesong Yang and twelve others, from Zhejiang University, the Hong Kong University of Science and Technology in Guangzhou, and one other institution. Published on the twenty-eighth of September. Its code repository is a placeholder: a readme, and nothing to run yet.

Generating long camera-controlled video means recovering content that was seen earlier, out of a visual history that keeps growing. Existing approaches take one of two routes. Some search the historical context implicitly, which makes memory access inefficient. Others reconstruct that history into a persistent 3D memory, which lets geometric errors accumulate over time.

GEAR rests on a division of labour. Geometry does not need to explain the scene. It only needs to determine where visual memory should be read from, while attention decides what should be recovered. Short for Geometry-Enabled Attention Routing, GEAR uses geometry as an explicit address for visual memory at the level of individual tokens. Geometry becomes the catalogue entry that says which shelf to look on, rather than a copy of the book.

Historical observations are never fused into a persistent global 3D representation. GEAR keeps them as frame latents and uses the geometry of each frame only to establish token-level correspondences with the target views, which avoids the persistent build-up of error that global fusion causes. Guided by those correspondences, Geometric Correspondence Attention selectively injects geometrically matched historical features into noisy target patches during denoising.

Occlusion needs its own handling, because a correspondence can make geometric sense and still point at something hidden. An Invisible Octree accumulates visibility evidence and rejects correspondences that are geometrically plausible but occluded.

Extensive experiments report state-of-the-art visual quality, precise camera control, and revisit consistency, so a place should look the same when the camera comes back to it. Together these enable minute-long video along challenging trajectories. The abstract gives no numerical margins.

The design choice is to ask less of geometry. Rather than trusting a reconstructed world to be right, GEAR uses geometry only as a pointer and leaves the recovery of content to attention, so any error in the pointer stays local to one lookup instead of being baked into a shared memory.

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Chapter 1 🧩 · The problem

A minute-long camera flight, and the scene must still remember itself. Here is the wall.

You are generating a long video as a camera roams a scene. When the camera returns somewhere it saw a minute ago, that place must look the same.

The road ahead

  1. 1 · The problem ~6 min A long camera-controlled video must show a revisited place the same way, but both usual memory designs either search too much or pile up geometry errors.
  2. 2 · What came before ~3 min
  3. 3 · The key idea ~9 min Use geometry only as an address saying where in past frames to read, and let attention decide what to recover from there.
  4. 4 · Choose your own research ~3 min
  5. 5 · Build the method ~8 min Store frames as latents, turn each frame's geometry into patch-to-patch matches, then let attention inject the matched features while denoising.
  6. 6 · What they found ~3 min
  7. 7 · The viva ~5 min Three questions from the examiners, answered in your own words. Win it for the paper’s trophy.
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