mlsanityData to 14 Oct 2026

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

arXiv 2511.12578 ↗First posted 16 Nov 2025cs.CVcited by 4

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 34 of its 41 references that are in the map. First posted on arXiv 16 Nov 2025.

Builds on

34 of 41 references.

References · 34

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2022Scalable Diffusion Models with Transformerscited by 6,054
  5. 2024Scaling Rectified Flow Transformers for High-Resolution Image Synthesiscited by 4,181
  6. 2025Wan: Open and Advanced Large-Scale Video Generative Modelscited by 2,708
  7. 2023VBench: Comprehensive Benchmark Suite for Video Generative Modelscited by 1,822
  8. 2023Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Modelscited by 1,476
  9. 2022CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformerscited by 985
  10. 2024Genie: Generative Interactive Environmentscited by 747
  11. 2024Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusioncited by 681
  12. 2021VideoGPT: Video Generation using VQ-VAE and Transformerscited by 658
22 more
  1. 2024Movie Gen: A Cast of Media Foundation Modelscited by 644
  2. 2025Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusioncited by 625
  3. 2023VideoPoet: A Large Language Model for Zero-Shot Video Generationcited by 492
  4. 2024From Slow Bidirectional to Fast Autoregressive Video Diffusion Modelscited by 433
  5. 2022Latent Video Diffusion Models for High-Fidelity Long Video Generationcited by 388
  6. 2024Pyramidal Flow Matching for Efficient Video Generative Modelingcited by 366
  7. 2025MAGI-1: Autoregressive Video Generation at Scalecited by 276
  8. 2025Seedance 1.0: Exploring the Boundaries of Video Generation Modelscited by 259
  9. 2025Rolling Forcing: Autoregressive Long Video Diffusion in Real Timecited by 234
  10. 2025Self-Forcing++: Towards Minute-Scale High-Quality Video Generationcited by 192
  11. 2024Autoregressive Video Generation without Vector Quantizationcited by 186
  12. 2023NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generationcited by 171
  13. 2025History-Guided Video Diffusioncited by 171
  14. 2024Rolling Diffusion Modelscited by 104
  15. 2023Elucidating the Exposure Bias in Diffusion Modelscited by 101
  16. 2025One-Minute Video Generation with Test-Time Trainingcited by 101
  17. 2025Hunyuan-GameCraft: High-dynamic Interactive Game Video Generation with Hybrid History Conditioncited by 98
  18. 2024MovieDreamer: Hierarchical Generation for Coherent Long Visual Sequencecited by 89
  19. 2024VideoStudio: Generating Consistent-Content and Multi-Scene Videoscited by 64
  20. 2025Waver: Wave Your Way to Lifelike Video Generationcited by 52
  21. 2025Captain Cinema: Towards Short Movie Generationcited by 48
  22. 2025Macro-from-Micro Planning for High-Quality and Parallelized Autoregressive Long Video Generationcited by 21

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.