Taming Teacher Forcing for Masked Autoregressive Video Generation
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Abstract (arXiv)
We introduce MAGI, a hybrid video generation framework that combines masked modeling for intra-frame generation with causal modeling for next-frame generation. Our key innovation, Complete Teacher Forcing (CTF), conditions masked frames on complete observation frames rather than masked ones (namely Masked Teacher Forcing, MTF), enabling a smooth transition from token-level (patch-level) to frame-level autoregressive generation. CTF significantly outperforms MTF, achieving a +23% improvement in FVD scores on first-frame conditioned video prediction. To address issues like exposure bias, we employ targeted training strategies, setting a new benchmark in autoregressive video generation. Experiments show that MAGI can generate long, coherent video sequences exceeding 100 frames, even when trained on as few as 16 frames, highlighting its potential for scalable, high-quality video generation.
Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 48 of its 58 references that are in the map. First posted on arXiv 21 Jan 2025.
Builds on
48 of 58 references.
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Citing papers per year: 2025: 18; 2026: 11.
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