Taming Teacher Forcing for Masked Autoregressive Video Generation
Authors: name: Deyu Zhou, name: Quan Sun, name: Yuang Peng, name: Kun Yan, name: Runpei Dong, name: Duomin Wang, name: Zheng Ge, name: Nan Duan, name: Xiangyu Zhang, name: Lionel M. Ni, name: Heung-Yeung Shum

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.