Spatia: Video Generation with Updatable Spatial Memory
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Cited by 20 in World models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Existing video generation models struggle to maintain long-term spatial and temporal consistency due to the dense, high-dimensional nature of video signals. To overcome this limitation, we propose Spatia, a spatial memory-aware video generation framework that explicitly preserves a 3D scene point cloud as persistent spatial memory. Spatia iteratively generates video clips conditioned on this spatial memory and continuously updates it through visual SLAM. This dynamic-static disentanglement design enhances spatial consistency throughout the generation process while preserving the model's ability to produce realistic dynamic entities. Furthermore, Spatia enables applications such as explicit camera control and 3D-aware interactive editing, providing a geometrically grounded framework for scalable, memory-driven video generation.
Key facts. Cited by 40 papers in the map (data through 14 Oct 2026). Builds on 101 of its 117 references that are in the map. First posted on arXiv 17 Dec 2025.
Builds on
101 of 117 references.
References · 101
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- 2024Open-Sora Plan: Open-Source Large Video Generation Modelcited by 296
50 most cited of 101 All 101 (JSON)
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Citing papers per year: 2026: 40.
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- 2026Code Plans, Diffusion Renders: Open-Ended Generative World Modeling22 Sep 2026
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- 2026Learning What to Recall: Adaptive Multi-Cue Episodic Memory for World Models28 Sep 2026
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- 2026From Pixels to States: Rethinking Interactive World Models as Game Engines15 Jul 2026
- 2026AlayaWorld: Long-Horizon and Playable Video World Generation7 Jul 2026
- 2026Current World Models Lack a Persistent State Core18 Jun 2026
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- 2026Omni-LiveAvatar: Minute-Level Real-Time Streaming Joint Audio-Video Avatar Generationcited by 1
- 2026ONE-SHOT: Compositional Human-Environment Video Synthesis via Spatial-Decoupled Motion Injection and Hybrid Context Integrationcited by 0
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