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arXiv · 2609.24984

WorldCrafter: Consistent Video World Model with Implicit 3D-aware Memory

Wangbo Yu·Kunhao Liu·Wenbo Hu·Shenghai Yuan·Chaoran Feng·Haiyang Zhou·Yukun Huang·Yiran Wang·Wang Zhao·Yingmin Luo·Ying Shan
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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.

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Wangbo Yu, Kunhao Liu, Wenbo Hu, Shenghai Yuan, Chaoran Feng, Haiyang Zhou, Yukun Huang, Yiran Wang, Wang Zhao, Yingmin Luo, Ying Shan. 2026-09-21. WorldCrafter: Consistent Video World Model with Implicit 3D-aware Memory. https://arxiv.org/abs/2609.24984

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