ABSTRACT Video snow removal is an important task in computer vision, as the snowflakes in videos reduce visibility and negatively affect the performance of outdoor visual systems. However, due to the complexity of real snowy scenarios, it is difficult to apply existing supervised learning-based methods to process real-world snowy videos. In this paper, we propose a novel two-stage video desnow network for the real world, called RVDNet. The first stage of RVDNet utilizes Spatial Feature Extraction Modules (SFEM) to extract the spatial features of the input frames. In the second stage, we design Spatial-Temporal Desnowing Modules (STDM) to remove snowflakes via spatio-temporal learning. Furthermore, we introduce the unsupervised domain adaptation module, which is embedded for aligning the feature space of real and synthetic data in the spatial and spatio-temporal domains, respectively. Experiments on the proposed SnowScape dataset prove that our method has superior desnow performance not only on synthetic data, but also in the real world.