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arXiv · 2605.31336

DecMem: Towards Minute-Long Consistent World Generation with Decoupled Memory

Zhenhao Yang·Xiaoshi Wu·Zhengyao Lv·Xiaoyu Shi·Xintao Wang·Pengfei Wan·Kun Gai·Kwan-Yee K. Wong
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Abstract

Recent advances in video generative models have promoted rapid progress in controllable world models. However, maintaining fine-grained spatio-temporal consistency under long-horizon reasoning remains a key challenge. In this work, we move beyond explicit 3D memory and coarse frame-level implicit modeling, and propose a fine-grained, learnable, and scalable memory for consistent world generation. We first identify two fundamental limitations of na\"ive learnable memory architectures in long-horizon extrapolation, namely computational inefficiency and attention dispersion. Through a systematic analysis of attention dispersion, we propose DecMem, a decoupled memory architecture that employs Sparse Global Memory for efficient fine-grained access to global history and Anchored Local Memory for stable and high-quality extrapolation. Extensive experiments demonstrate that DecMem significantly outperforms current state-of-the-art methods. By ensuring precise and efficient long-term memory and achieving superior extrapolation capabilities, DecMem enables minute-level controllable long video generation with high fidelity and consistency.

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Zhenhao Yang, Xiaoshi Wu, Zhengyao Lv, Xiaoyu Shi, Xintao Wang, Pengfei Wan, Kun Gai, Kwan-Yee K. Wong. 2026-05-29. DecMem: Towards Minute-Long Consistent World Generation with Decoupled Memory. https://arxiv.org/abs/2605.31336

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