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Research PaperResearchia:202609.22002

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

Wangbo Yu

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-conditione...

Submitted: September 22, 2026Subjects: AI; Artificial Intelligence

Description / Details

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.


Source: arXiv:2609.24984v1 - http://arxiv.org/abs/2609.24984v1 PDF: https://arxiv.org/pdf/2609.24984v1 Original Link: http://arxiv.org/abs/2609.24984v1

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Date:
Sep 22, 2026
Topic:
Artificial Intelligence
Area:
AI
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