Abstract: 4D reconstruction from a single monocular video is an important but challenging task due to its inherent under-constrained nature. While most existing 4D reconstruction methods focus on multi-camera settings, they always suffer from limited multi-view information in monocular videos. Recent studies have attempted to mitigate the ill-posed problem by incorporating data-driven priors as additional supervision. However, they require hours of optimization to align the splatted 2D feature maps of explicit Gaussians with various priors, which limits the range of applications. To address the time-consuming issue, we propose \textbf{4D-Fly}, an efficient and effective framework for reconstructing the 4D scene from a monocular video (hundreds of frames within 6 minutes), more than \textbf{20} $\mathbf{\times}$ \textbf{faster} and even achieving higher quality than previous optimization methods. Our key insight is to unleash the explicit property of Gaussian primitives and directly apply data priors to them. Specifically, we build a streaming 4D reconstruction paradigm that includes: propagating existing Gaussian to the next timestep with an anchor-based strategy, expanding the 4D scene map with the canonical Gaussian map, and an efficient 4D scene optimization process to further improve visual quality and motion accuracy. Extensive experiments demonstrate the superiority of our 4D-Fly over state-of-the-art methods in terms of speed and quality.