InfinityStar: Unified Spacetime AutoRegressive Modeling for Visual Generation

Jinlai Liu, Jian Han, Bin Yan, Wuhui, Fengda Zhu, Xing Wang, Yi Jiang, BINGYUE PENG, Zehuan Yuan

Keywords: Visual Synthesis, Visual AutoRegressive Modeling, Text to Video Generation, Generative models

TL;DR: InfinityStar is the first discrete autoregressive video generator capable of producing industrial-level 720p videos.

Abstract: We introduce InfinityStar, 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, InfinityStar 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, InfinityStar is the first discrete autoregressive video generator capable of producing industrial-level 720p videos. We release all code and models to foster further research in efficient, high-quality video generation.

Primary Area: Deep learning (e.g., architectures, generative models, optimization for deep networks, foundation models, LLMs)

Submission Number: 2539