MemLearner: Learning to Query Context memory for Video World Models

• URL: http://arxiv.org/abs/2606.31734v1
• Date: Tue, 30 Jun 2026 14:31:32 GMT
• Title: MemLearner: Learning to Query Context memory for Video World Models
• Abstract summary: We propose MemLearner, a learning-based adaptive context query method using query tokens to bridge context and predicted tokens.<n>MemLearner exploits pre-trained visual priors without training additional modules from scratch, and incorporates efficient strategies for training and inference.<n>Extensive experiments demonstrate that MemLearner significantly outperforms prior video world models in terms of scene consistency and memory.
• Score: 54.156384983859816
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video World Models are interactive video generation models that predict future world states based on user actions and history video frames. A critical challenge in video world models is the lack of memory, causing inconsistent generated scenes over extended durations. Previous methods explored rule-based context frame retrieval as memory, but they fail to generalize in scenarios with scene occlusions and dynamic objects. We propose MemLearner, a learning-based adaptive context query method using query tokens to bridge context and predicted tokens. By leveraging the video generation model itself for context querying, MemLearner exploits pre-trained visual priors without training additional modules from scratch, and incorporates efficient strategies for training and inference. We collect a dataset of long videos with scene occlusions and dynamic objects, paired with camera pose annotations, and propose a multi-dataset training strategy leveraging both annotated rendered and unannotated real-world videos. Extensive experiments demonstrate that MemLearner significantly outperforms prior video world models in terms of scene consistency and memory, particularly under challenging occlusion and dynamic scenarios.

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