We present Functional Mean Flow (FMF) as a one-step generative model defined in infinite-dimensional Hilbert space. FMF extends the one-step Mean Flow framework to functional domains by providing a theoretical formulation for Functional Flow Matching and a practical implementation for efficient training and sampling. We also introduce an x1-prediction variant that improves stability over the original u-prediction form. The resulting framework is a practical one-step Flow Matching method applicable to a wide range of functional data generation tasks such as time series, images, PDEs, and 3D geometry.
@article{arxiv.2511.12898,
title = {Functional Mean Flow in Hilbert Space},
author = {Zhiqi Li and Yuchen Sun and Greg Turk and Bo Zhu},
journal= {arXiv preprint arXiv:2511.12898},
year = {2025}
}