VideoAR: Autoregressive Video Generation via Next-Frame & Scale Prediction

• URL: http://arxiv.org/abs/2601.05966v2
• Date: Wed, 14 Jan 2026 14:12:54 GMT
• Title: VideoAR: Autoregressive Video Generation via Next-Frame & Scale Prediction
• Authors: Longbin Ji, Xiaoxiong Liu, Junyuan Shang, Shuohuan Wang, Yu Sun, Hua Wu, Haifeng Wang,
• Abstract summary: VideoAR is the first large-scale Visual Autoregressive framework for video generation that combines multi-scale next-frame prediction with autoregressive modeling.<n>VideoAR disentangles spatial and temporal dependencies by integrating intra-frame VAR with causal next-frame prediction, supported by a 3D multi-scale tokenizer.<n> Empirically, VideoAR achieves new state-of-the-art results improving resolutions among autoregressive models, FVD on UCF-101 from 99.5 to 88.6 while reducing inference steps over 10x, and reaching a VBench score of 81.74-competitive with diffusion-based
• Score: 31.191310873846177
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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