TempoMaster: Efficient Long Video Generation via Next-Frame-Rate Prediction

Yukuo Ma, Cong Liu, Junke Wang, Junqi Liu, Haibin Huang +3 more
11/16/2025
cs.CV

No official code found. Best implementation: scottykma/TempoMaster (10 stars, MIT, last commit 2025-12-19).

Abstract

We present TempoMaster, a novel framework that formulates long video generation as next-frame-rate prediction. Specifically, we first generate a low-frame-rate clip that serves as a coarse blueprint of the entire video sequence, and then progressively increase the frame rate to refine visual details and motion continuity. During generation, TempoMaster employs bidirectional attention within each frame-rate level while performing autoregression across frame rates, thus achieving long-range temporal coherence while enabling efficient and parallel synthesis. Extensive experiments demonstrate that TempoMaster establishes a new state-of-the-art in long video generation, excelling in both visual and temporal quality.

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Code Implementations(1)

MIT

[CVPR2026] Efficient Long Video Generation via Next-Frame-Rate Prediction

101PythonNov 25, 202510 months agoMIT

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Cite this paper

@article{ma2025tempomaster,
  title  = {TempoMaster: Efficient Long Video Generation via Next-Frame-Rate Prediction},
  author = {Yukuo Ma and Cong Liu and Junke Wang and Junqi Liu and Haibin Huang and Zuxuan Wu and Chi Zhang and Xuelong Li},
  year   = {2025},
  eprint = {2511.12578},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2511.12578v3}
}

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