VideoSSM: Autoregressive Long Video Generation with Hybrid State-Space Memory
Autoregressive (AR) diffusion enables streaming, interactive long-video generation by producing frames causally, yet maintaining coherence over minute-scale horizons remains challenging due to accumulated errors, motion drift, and content repetition. We approach this problem from a memory perspective, treating video synthesis as a recurrent dynamical process that requires coordinated short- and long-term context. We propose VideoSSM, a Long Video Model that unifies AR diffusion with a hybrid state-space memory. The state-space model (SSM) serves as an evolving global memory of scene dynamics across the entire sequence, while a context window provides local memory for motion cues and fine details. This hybrid design preserves global consistency without frozen, repetitive patterns, supports prompt-adaptive interaction, and scales in linear time with sequence length. Experiments on short- and long-range benchmarks demonstrate state-of-the-art temporal consistency and motion stability among autoregressive video generator especially at minute-scale horizons, enabling content diversity and interactive prompt-based control, thereby establishing a scalable, memory-aware framework for long video generation.
69395a75dfc35938ba12a23e
Yifei Yu
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2025-12-11T10:13:37.633Z
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Yifei Yu
yuyifei
user
yuyifei
69395a75dfc35938ba12a23f
Xiaoshan Wu
69395a75dfc35938ba12a240
Xinting Hu
69395a75dfc35938ba12a241
Tao Hu
69395a75dfc35938ba12a242
Yangtian Sun
69395a75dfc35938ba12a243
Xiaoyang Lyu
69395a75dfc35938ba12a244
Bo Wang
69395a75dfc35938ba12a245
Lin Ma
69395a75dfc35938ba12a246
Yuewen Ma
69395a75dfc35938ba12a247
Zhongrui Wang
69395a75dfc35938ba12a248
Xiaojuan Qi
VideoSSM: Autoregressive Long Video Generation with Hybrid State-Space Memory
Autoregressive (AR) diffusion enables streaming, interactive long-video generation by producing frames causally, yet maintaining coherence over minute-scale horizons remains challenging due to accumulated errors, motion drift, and content repetition. We approach this problem from a memory perspective, treating video synthesis as a recurrent dynamical process that requires coordinated short- and long-term context. We propose VideoSSM, a Long Video Model that unifies AR diffusion with a hybrid state-space memory. The state-space model (SSM) serves as an evolving global memory of scene dynamics across the entire sequence, while a context window provides local memory for motion cues and fine details. This hybrid design preserves global consistency without frozen, repetitive patterns, supports prompt-adaptive interaction, and scales in linear time with sequence length. Experiments on short- and long-range benchmarks demonstrate state-of-the-art temporal consistency and motion stability among autoregressive video generator especially at minute-scale horizons, enabling content diversity and interactive prompt-based control, thereby establishing a scalable, memory-aware framework for long video generation.
VideoSSM, a hybrid state-space memory model combining AR diffusion, achieves state-of-the-art temporal consistency and motion stability in long-video generation by coordinating short- and long-term context.
autoregressive diffusion VideoSSM state-space model global memory local memory scene dynamics context window motion cues fine details temporal consistency motion stability content diversity prompt-based control