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WorldCrafter advances video world models by introducing an implicit 3D-aware memory mechanism that maintains long-horizon consistency across varying viewpoints without relying on explicit depth correspondences. This approach allows for streaming scene exploration from single inputs while effectively managing token budgets through pose-conditioned readout modules. The method represents a significant step toward robust, interactive virtual environments driven by advanced generative AI techniques.
WorldCrafter: Consistent Video World Model with Implicit 3D-aware Memory
arXiv:2609.24984v1 Announce Type: cross Abstract: Video world models enable interactive exploration of dynamic environments, yet struggle to respect prior observations over long horizons and across viewpoints. We present WorldCrafter, a video world model that learns a camera-queryable implicit 3D-aware memory for this purpose. The key insight is to let the requested viewpoint shape how multi-view evidence is compressed into the video generator's limited token budget. Trained jointly with the video generator, a memory encoder and pose-conditioned readout module integrate historical observations into a fixed set of target view-specific tokens before denoising, without explicit depth-based correspondences. By combining this memory with recent temporal context and few-step distillation, WorldCrafter enables streaming scene exploration from a single input image or text prompt. Experiments across static and dynamic scenes show substantial gains in long-horizon consistency and camera-control accuracy while preserving visual quality during minute-scale exploration.
Abstract:Video world models enable interactive exploration of dynamic environments, yet struggle to respect prior observations over long horizons and across viewpoints. We present WorldCrafter, a video world model that learns a camera-queryable implicit 3D-aware memory for this purpose. The key insight is to let the requested viewpoint shape how multi-view evidence is compressed into the video generator's limited token budget. Trained jointly with the video generator, a memory encoder and pose-conditioned readout module integrate historical observations into a fixed set of target view-specific tokens before denoising, without explicit depth-based correspondences. By combining this memory with recent temporal context and few-step distillation, WorldCrafter enables streaming scene exploration from a single input image or text prompt. Experiments across static and dynamic scenes show substantial gains in long-horizon consistency and camera-control accuracy while preserving visual quality during minute-scale exploration.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Graphics (cs.GR)
arXiv:2609.24984 [cs.CV]
(or arXiv:2609.24984v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.24984 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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