LongVie 2: Multimodal Controllable Ultra-Long Video World Model
Building video world models upon pretrained video generation systems represents an important yet challenging step toward general spatiotemporal intelligence. A world model should possess three essential properties: controllability, long-term visual quality, and temporal consistency. To this end, we take a progressive approach-first enhancing controllability and then extending toward long-term, high-quality generation. We present LongVie 2, an end-to-end autoregressive framework trained in three stages: (1) Multi-modal guidance, which integrates dense and sparse control signals to provide implicit world-level supervision and improve controllability; (2) Degradation-aware training on the input frame, bridging the gap between training and long-term inference to maintain high visual quality; and (3) History-context guidance, which aligns contextual information across adjacent clips to ensure temporal consistency. We further introduce LongVGenBench, a comprehensive benchmark comprising 100 high-resolution one-minute videos covering diverse real-world and synthetic environments. Extensive experiments demonstrate that LongVie 2 achieves state-of-the-art performance in long-range controllability, temporal coherence, and visual fidelity, and supports continuous video generation lasting up to five minutes, marking a significant step toward unified video world modeling.
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Jianxiong Gao
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2025-12-19T09:07:49.132Z
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Jianxiong Gao
Jianxiong
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Jianxiong
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Zhaoxi Chen
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Xian Liu
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Junhao Zhuang
claimed_verified
2025-12-16T09:39:51.620Z
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JunhaoZhuang
JunhaoZhuang
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JunhaoZhuang
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Chengming Xu
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Jianfeng Feng
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Yu Qiao
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Yanwei Fu
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Chenyang Si
claimed_verified
2025-12-16T09:39:49.025Z
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ChenyangSi
ChenyangSi
user
ChenyangSi
6940d44165f1e24a1178053e
Ziwei Liu
LongVie 2: Multimodal Controllable Ultra-Long Video World Model
Building video world models upon pretrained video generation systems represents an important yet challenging step toward general spatiotemporal intelligence. A world model should possess three essential properties: controllability, long-term visual quality, and temporal consistency. To this end, we take a progressive approach-first enhancing controllability and then extending toward long-term, high-quality generation. We present LongVie 2, an end-to-end autoregressive framework trained in three stages: (1) Multi-modal guidance, which integrates dense and sparse control signals to provide implicit world-level supervision and improve controllability; (2) Degradation-aware training on the input frame, bridging the gap between training and long-term inference to maintain high visual quality; and (3) History-context guidance, which aligns contextual information across adjacent clips to ensure temporal consistency. We further introduce LongVGenBench, a comprehensive benchmark comprising 100 high-resolution one-minute videos covering diverse real-world and synthetic environments. Extensive experiments demonstrate that LongVie 2 achieves state-of-the-art performance in long-range controllability, temporal coherence, and visual fidelity, and supports continuous video generation lasting up to five minutes, marking a significant step toward unified video world modeling.
LongVie 2, an end-to-end autoregressive framework, enhances controllability, visual quality, and temporal consistency in video world models through three progressive training stages.
autoregressive framework multi-modal guidance degradation-aware training history-context guidance video world models controllability visual quality temporal consistency LongVGenBench state-of-the-art performance temporal coherence visual fidelity