TempoMaster: Efficient Long Video Generation via Next-Frame-Rate Prediction
Yukuo Ma, Cong Liu, Junke Wang, Junqi Liu, Haibin Huang, Zuxuan Wu, Chi Zhang, Xuelong Li
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.
BibTeX
@inproceedings{cvpr2026_tempomastereffic,
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},
booktitle = {CVPR 2026},
year = {2026}
}