GeoFlow: Efficient Driving Video Generation via Geometry-Aligned Priors

Abstract

Generative models like Di_x001B_usion Models and Flow Matchinghave demonstrated remarkable capabilities in synthesizing high-_x001C_delitydriving videos, but are severely constrained by high inference latencydue to the requirement of extensive sampling steps. We argue that thisine_x001E_ciency stems from the prevailing reliance on a standard Gaussiansource distribution, where consecutive frames are initialized as indepen-dent Gaussian noise. This paradigm disregards the rich spatiotemporalcorrelations inherent in driving videos, compelling the model to regener-ate deterministic scene structures existing in previous frames from noise,which is both computationally redundant and prone to geometric incon-sistency. To address this problem, we propose GeoFlow, a novel frame-work designed to achieve e_x001E_cient driving video generation by harnessingexplicit geometric priors. Instead of sampling from standard Gaussiannoise, we leverage multi-view geometry and spatially-adaptive noise in-jection to construct a Geometry-Aligned Prior (GAP) distribution asstarting point. This initialization bridges the gap between source dis-tribution and data distribution, yielding a signi_x001C_cantly straighter andshorter sampling trajectory. Extensive experiments demonstrate that Ge-oFlow can achieve remarkable e_x001E_ciency of both training and inference:merely several hours of _x001C_ne-tuning on baseline models can signi_x001C_cantlyboost few-step generation quality, while fully converged training drasti-cally reduces number of inference steps required for state-of-the-art videogeneration.