VideoSSM: Autoregressive Long Video Generation with Hybrid State-Space Memory
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Abstract (arXiv)
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.
Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). Builds on 58 of its 62 references that are in the map. First posted on arXiv 4 Dec 2025.
Builds on
58 of 62 references.
References · 58
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- 2025Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusioncited by 569
- 2024LTX-Video: Realtime Video Latent Diffusioncited by 549
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- 2024Gated Delta Networks: Improving Mamba2 with Delta Rulecited by 483
- 2023VideoPoet: A Large Language Model for Zero-Shot Video Generationcited by 479
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- 2022Latent Video Diffusion Models for High-Fidelity Long Video Generationcited by 385
- 2024Pyramidal Flow Matching for Efficient Video Generative Modelingcited by 354
- 2015Deep multi-scale video prediction beyond mean square errorcited by 297
- 2025MAGI-1: Autoregressive Video Generation at Scalecited by 262
- 2022Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformercited by 244
- 2024StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Textcited by 242
- 2025LongLive: Real-time Interactive Long Video Generationcited by 237
- 2023SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Predictioncited by 231
- 2025SkyReels-V2: Infinite-length Film Generative Modelcited by 218
- 2025Rolling Forcing: Autoregressive Long Video Diffusion in Real Timecited by 209
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- 2024Autoregressive Video Generation without Vector Quantizationcited by 180
- 2025Self-Forcing++: Towards Minute-Scale High-Quality Video Generationcited by 176
- 2023FreeNoise: Tuning-Free Longer Video Diffusion via Noise Reschedulingcited by 173
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- 2025Context as Memory: Scene-Consistent Interactive Long Video Generation with Memory Retrievalcited by 157
- 2025WorldMem: Long-term Consistent World Simulation with Memorycited by 152
- 2024VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Modelscited by 124
- 2025Video World Models with Long-term Spatial Memorycited by 124
- 2025Long-Context Autoregressive Video Modeling with Next-Frame Predictioncited by 119
- 2025Long Context Tuning for Video Generationcited by 110
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- 2024FreeLong: Training-Free Long Video Generation with SpectralBlend Temporal Attentioncited by 87
- 2022NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesiscited by 71
50 most cited of 58 All 58 (JSON)
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Citing papers per year: 2025: 2; 2026: 37.
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