WorldPack: Compressed Memory Improves Spatial Consistency in Video World Modeling

• URL: http://arxiv.org/abs/2512.02473v1
• Date: Tue, 02 Dec 2025 07:06:23 GMT
• Title: WorldPack: Compressed Memory Improves Spatial Consistency in Video World Modeling
• Authors: Yuta Oshima, Yusuke Iwasawa, Masahiro Suzuki, Yutaka Matsuo, Hiroki Furuta,
• Abstract summary: We propose WorldPack, a video world model with efficient compressed memory.<n>WorldPack significantly improves spatial consistency, fidelity, and quality in long-term generation.<n>Our performance is evaluated with LoopNav, a benchmark on Minecraft.
• Score: 42.52474988220278
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video world models have attracted significant attention for their ability to produce high-fidelity future visual observations conditioned on past observations and navigation actions. Temporally- and spatially-consistent, long-term world modeling has been a long-standing problem, unresolved with even recent state-of-the-art models, due to the prohibitively expensive computational costs for long-context inputs. In this paper, we propose WorldPack, a video world model with efficient compressed memory, which significantly improves spatial consistency, fidelity, and quality in long-term generation despite much shorter context length. Our compressed memory consists of trajectory packing and memory retrieval; trajectory packing realizes high context efficiency, and memory retrieval maintains the consistency in rollouts and helps long-term generations that require spatial reasoning. Our performance is evaluated with LoopNav, a benchmark on Minecraft, specialized for the evaluation of long-term consistency, and we verify that WorldPack notably outperforms strong state-of-the-art models.

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