AnchorWeave is a memory‑augmented video generation framework that replaces a single misaligned global memory with multiple clean local geometric memories. It retrieves local memories based on coverage of the target trajectory and integrates them through a multi‑anchor weaving controller during generation. Experiments show that this approach improves long‑term scene consistency while preserving visual quality, with ablation studies confirming the benefits of local conditioning, multi‑anchor control, and coverage‑driven retrieval.
By Zun Wang, Han Lin, Jaehong Yoon, Jaemin Cho, Yue Zhang, Mohit Bansal
arXiv:2608. 07408v1 Announce Type: cross Abstract: We study visual persistence in interactive video world models.
By Xindi Wu, Sven Elflein, James Lucas, Olga Russakovsky, Laura Leal-Taix\'e, Despoina Paschalidou, Jonathan Lorraine, Aljo\v{s}a O\v{s}ep
arXiv:2610.17434v1 Announce Type: cross
Abstract: Recent advances in video world models have enabled plausible interactive environments, yet long-horizon generation places a fundamental demand on vis...
By Junyi Wu, Zekun Li, Chenhongyi Yang, Chen Guo, Lingchen Yang, Yongan Zhang, Xinyi Zhang, Hongbing Hu, Bartek Rymkowski, Srinath Sridhar, Yan Yan, Xijie Huang
LayerRecall is a memory router for autoregressive video diffusion that selectively retrieves and injects historical key/value states into specific layers of the model, based on the current context. It addresses the problem that existing memory mechanisms expose nonlocal history but do not guarantee effective use, by recognizing that different layers prefer current, recent, or distant context. The method, combined with Cross‑Horizon Prediction Matching, achieves state‑of‑the‑art long‑range consistency on MemoBench and MovieBench while maintaining local continuity and incurring negligible inference overhead.
By Yixuan Ding, Jiahao Kong, Wei Huang, Ruijie Quan, Yi Yang
The paper introduces Learning-to-Retrieve (L2R), a method that embeds memory retrieval directly into video world models. By using the model’s internal state as a memory and employing a camera‑conditioned retrieval gate and a retrieval trigger supervised by a 3D re‑visibility signal, the approach learns when and what historical information to access. This eliminates the need for external memory systems and improves long‑term scene consistency across various base models and camera‑revisit benchmarks.
By JiaKui Hu, Tailai Chen, Yuqi Pan, Xuerui Qiu, Jialun Liu, Xiao Cao, Zhenxin Zhu, Guang Chen, Hangjun Ye, Bing Wang, Yanye Lu
The paper introduces a decoupled memory control framework for spatially consistent long‑horizon video generation. By separating memory conditioning from generation, it uses a lightweight memory branch to learn spatial consistency from past observations and a per‑frame cross‑attention mechanism to condition each frame on the most relevant historical data. A camera‑aware gating mechanism further ensures memory is used only when meaningful references exist, resulting in lower training costs, higher spatial consistency, and state‑of‑the‑art visual quality.
By Yanjun Guo, Zhengqiang Zhang, Pengfei Wang, Xinyue Liang, Zhiyuan Ma, Lei Zhang
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 m...
WorldCrafter is a video world model that introduces a camera‑queryable implicit 3D‑aware memory to improve long‑horizon consistency and viewpoint control. The model compresses multi‑view evidence into a limited token budget shaped by the requested viewpoint, integrating historical observations via a memory encoder and pose‑conditioned readout before denoising. Experiments on static and dynamic scenes demonstrate significant gains in consistency and camera‑control accuracy while maintaining visual quality during minute‑scale exploration.
By Wangbo Yu, Kunhao Liu, Wenbo Hu, Shenghai Yuan, Chaoran Feng, Haiyang Zhou, Yukun Huang, Yiran Wang, Wang Zhao, Yingmin Luo, Ying Shan
LoG-VGGT is a memory‑efficient framework for long‑sequence 3D reconstruction that balances local temporal modeling with global camera consistency. It uses cross‑window attention in a small subset of transformer blocks to propagate information across adjacent temporal windows while keeping memory usage bounded. A global camera consistency refinement module further improves long‑horizon pose stability by enforcing scene‑level constraints through cross‑attention between camera and compact register tokens, leading to better depth accuracy and robust camera pose estimation on multiple benchmarks.
By Jingke Zhou, Chenhang Ma, Zhizhou Zhong, Mingkai Liu, Zhuang Zhou, Yicheng ji, Binghua Su, Bo Cai, Xianliang Huang
arXiv:2610.19257v1 Announce Type: new
Abstract: Memory for long-horizon video generation must satisfy two competing requirements: it must remain bounded so generation can continue indefinitely, while...
By Eren Cetin, Julius Erbach, Vukasin Bozic, Hyojun Go, Zixiang Zhao, Nikolai Kalischek, Federico Tombari, Konrad Schindler, Dominik Narnhofer
arXiv:2608. 13492v1 Announce Type: new Abstract: This report presents an improved version of AlayaWorld.
By AlayaWorld Team, Kaipeng Zhang, Chuanhao Li, Yifan Zhan, Yongtao Ge, Yuanyang Yin, Jiaming Tan, Kang He, Liaoyuan Fan, Mingliang Zhai, Ruicong Liu, Xiaojie Xu, Xuangeng Chu, Zhen Li, Zhengyuan Lin, Zhixiang Wang, Zian Meng, Zihui Gao
arXiv:2512. 02473v2 Announce Type: replace-cross 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.
By Yuta Oshima, Yusuke Iwasawa, Masahiro Suzuki, Yutaka Matsuo, Hiroki Furuta