VideoAR: Autoregressive Video Generation via Next-Frame & Scale Prediction
Recent advances in video generation have been dominated by diffusion and flow-matching models, which produce high-quality results but remain computationally intensive and difficult to scale. In this work, we introduce VideoAR, the first large-scale Visual Autoregressive (VAR) framework for video generation that combines multi-scale next-frame prediction with autoregressive modeling. VideoAR disentangles spatial and temporal dependencies by integrating intra-frame VAR modeling with causal next-frame prediction, supported by a 3D multi-scale tokenizer that efficiently encodes spatio-temporal dynamics. To improve long-term consistency, we propose Multi-scale Temporal RoPE, Cross-Frame Error Correction, and Random Frame Mask, which collectively mitigate error propagation and stabilize temporal coherence. Our multi-stage pretraining pipeline progressively aligns spatial and temporal learning across increasing resolutions and durations. Empirically, VideoAR achieves new state-of-the-art results among autoregressive models, improving FVD on UCF-101 from 99.5 to 88.6 while reducing inference steps by over 10x, and reaching a VBench score of 81.74-competitive with diffusion-based models an order of magnitude larger. These results demonstrate that VideoAR narrows the performance gap between autoregressive and diffusion paradigms, offering a scalable, efficient, and temporally consistent foundation for future video generation research.
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Longbin Ji
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2026-02-05T10:57:39.222Z
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Longbin Ji
robingg1
user
robingg1
69645e19138cc47cbd765240
Xiaoxiong Liu
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Junyuan Shang
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2026-01-12T10:34:45.302Z
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Junyuan Shang
sjy1203
user
sjy1203
69645e19138cc47cbd765242
Shuohuan Wang
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Yu Sun
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Hua Wu
69645e19138cc47cbd765245
Haifeng Wang
VideoAR: Autoregressive Video Generation via Next-Frame & Scale Prediction
Recent advances in video generation have been dominated by diffusion and flow-matching models, which produce high-quality results but remain computationally intensive and difficult to scale. In this work, we introduce VideoAR, the first large-scale Visual Autoregressive (VAR) framework for video generation that combines multi-scale next-frame prediction with autoregressive modeling. VideoAR disentangles spatial and temporal dependencies by integrating intra-frame VAR modeling with causal next-frame prediction, supported by a 3D multi-scale tokenizer that efficiently encodes spatio-temporal dynamics. To improve long-term consistency, we propose Multi-scale Temporal RoPE, Cross-Frame Error Correction, and Random Frame Mask, which collectively mitigate error propagation and stabilize temporal coherence. Our multi-stage pretraining pipeline progressively aligns spatial and temporal learning across increasing resolutions and durations. Empirically, VideoAR achieves new state-of-the-art results among autoregressive models, improving FVD on UCF-101 from 99.5 to 88.6 while reducing inference steps by over 10x, and reaching a VBench score of 81.74-competitive with diffusion-based models an order of magnitude larger. These results demonstrate that VideoAR narrows the performance gap between autoregressive and diffusion paradigms, offering a scalable, efficient, and temporally consistent foundation for future video generation research.
VideoAR presents a large-scale visual autoregressive framework for video generation that combines multi-scale next-frame prediction with autoregressive modeling, achieving state-of-the-art results with improved efficiency and temporal consistency.
Visual Autoregressive diffusion models flow-matching models video generation multi-scale next-frame prediction autoregressive modeling intra-frame VAR modeling causal next-frame prediction 3D multi-scale tokenizer temporal dependencies spatial dependencies Multi-scale Temporal RoPE Cross-Frame Error Correction Random Frame Mask multi-stage pretraining pipeline FVD VBench