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Video World Models with Long-term Spatial Memory
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Video World Models with Long-term Spatial Memory
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Emerging world models autoregressively generate video frames in response to actions, such as camera movements and text prompts, among other control signals. Due to limited temporal context window sizes, these models often struggle to maintain scene consistency during revisits, leading to severe forgetting of previously generated environments. Inspired by the mechanisms of human memory, we introduce a novel framework to enhancing long-term consistency of video world models through a geometry-grounded long-term spatial memory. Our framework includes mechanisms to store and retrieve information from the long-term spatial memory and we curate custom datasets to train and evaluate world models with explicitly stored 3D memory mechanisms. Our evaluations show improved quality, consistency, and context length compared to relevant baselines, paving the way towards long-term consistent world generation.
Forward citations
Cited by 35 Pith papers
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From Synchrony to Sequence: Exo-to-Ego Generation via Interpolation
Interpolating only the video frames between synchronized exo and ego clips already turns discontinuous cross-view generation into continuous sequence modeling and measurably improves diffusion-based Exo2Ego synthesis.
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Rein3D: Reinforced 3D Indoor Scene Generation with Panoramic Video Diffusion Models
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DecMem: Towards Minute-Long Consistent World Generation with Decoupled Memory
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World-R1: Reinforcing 3D Constraints for Text-to-Video Generation
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World-R1: Reinforcing 3D Constraints for Text-to-Video Generation
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World-R1: Reinforcing 3D Constraints for Text-to-Video Generation
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