STARFlow-V: End-to-End Video Generative Modeling with Normalizing Flows

STARFlow-V: End-to-End Video Generative Modeling with Normalizing Flows

AuthorsJiatao Gu†, Ying Shen‡**, Tianrong Chen, Laurent Dinh, Yuyang Wang, Miguel Ángel Bautista, David Berthelot, Josh Susskind, Shuangfei Zhai

STARFlow-V: End-to-End Video Generative Modeling with Normalizing Flows

AuthorsJiatao Gu†, Ying Shen‡**, Tianrong Chen, Laurent Dinh, Yuyang Wang, Miguel Ángel Bautista, David Berthelot, Josh Susskind, Shuangfei Zhai

Normalizing flows (NFs) are end-to-end likelihood-based generative models for continuous data, and have recently regained attention with encouraging progress on image generation. Yet in the video generation domain, where spatiotemporal complexity and computational cost are substantially higher, state-of-the-art systems almost exclusively rely on diffusion-based models. In this work, we revisit this design space by presenting STARFlow-V, a normalizing flow-based video generator with substantial benefits such as end-to-end learning, robust causal prediction, and native likelihood estimation. Building upon the recently proposed STARFlow, STARFlow-V operates in the spatiotemporal latent space with a global-local architecture which restricts causal dependencies to a global latent space while preserving rich local within-frame interactions. This eases error accumulation over time, a common pitfall of standard autoregressive diffusion model generation. Additionally, we propose flow-score matching, which equips the model with a light-weight causal denoiser to improve the video generation consistency in an autoregressive fashion. To improve the sampling efficiency, STARFlow-V employs a video-aware Jacobi iteration scheme that recasts inner updates as parallelizable iterations without breaking causality. Thanks to the invertible structure, the same model can natively support text-to-video, image-to-video as well as video-to-video generation tasks. Empirically, STARFlow-V achieves strong visual fidelity and temporal consistency with practical sampling throughput relative to diffusion-based baselines. These results present the first evidence, to our knowledge, that NFs are capable of high-quality autoregressive video generation, establishing them as a promising research direction for building world models.

STIV: Scalable Text and Image Conditioned Video Generation

August 1, 2025research area Computer Vision, research area Methods and Algorithms

The field of video generation has made remarkable advancements, yet there remains a pressing need for a clear, systematic recipe that can guide the development of robust and scalable models. In this work, we present a comprehensive study that systematically explores the interplay of model architectures, training recipes, and data curation strategies, culminating in a simple and scalable text-image-conditioned video generation method, named STIV…

STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis

June 30, 2025research area Computer Vision, research area Methods and Algorithmsconference NeurIPS

We present STARFlow, a scalable generative model based on normalizing flows that achieves strong performance in high-resolution image synthesis. The core of STARFlow is Transformer Autoregressive Flow (TARFlow), which combines the expressive power of normalizing flows with the structured modeling capabilities of Autoregressive Transformers. We first establish the theoretical universality of TARFlow for modeling continuous distributions. Building…