Infinity*: Uniﬁed Spacetime AutoRegressive Modeling for Visual Generation
Jinlai Liu, Jian Han, Bin Yan, Yi Jiang, Hui Wu, Fengda Zhu, Xing Wang, BINGYUE PENG, Zehuan Yuan
We introduce Infinity*, a unified spacetime autoregressive framework for high-resolution image and dynamic video synthesis. Building on the recent success of autoregressive modeling in both vision and language, our purely discrete approach jointly captures spatial and temporal dependencies within a single architecture. This unified design naturally supports a variety of generation tasks such as text-to-image, text-to-video, image-to-video, and long-duration video synthesis via straightforward temporal autoregression. Through extensive experiments, Infinity* scores 83.74 on VBench, outperforming all autoregressive models by large margins, even surpassing diffusion competitors like HunyuanVideo. Without extra optimizations, our model generates a 5s, 720p video approximately 10$\times$ faster than leading diffusion-based methods. To our knowledge, Infinity* is the first discrete autoregressive video generator capable of producing industrial-grade 720p videos up to 10 seconds. We will release all code and models to foster further research in efficient, high-quality video generation.