Novel view synthesis from limited observations remains a crucial and ongoing challenge. In the realm of NeRF-based few-shot view synthesis, there is often a trade-off between the accuracy of the synthesized view and the efficiency of the 3D representation. To tackle this dilemma, we introduce a Few-Shot view synthesis framework based on 3D Gaussian Splatting, which facilitates real-time, photo-realistic synthesis from a minimal number of training views. FSGS employs an innovative Proximity-guided Gaussian Unpooling, specifically designed for sparse-view settings, to bridge the gap presented by the sparse initial point sets. This method involves the strategic placement of new Gaussians between existing ones, guided by a Gaussian proximity score, enhancing the adaptive density control. We have identified that Gaussian optimization can sometimes result in overly smooth textures and a propensity for overfitting when training views are limited. To mitigate these issues, FSGS introduces the synthesis of virtual views to replicate the parallax effect experienced during training, coupled with geometric regularization applied across both actual training and synthesized viewpoints. This strategy ensures that new Gaussians are placed in the most representative locations, fostering more accurate and detailed scene reconstruction. Our comprehensive evaluation across various datasets-including NeRF-Synthetic, LLFF, Shiny, and Mip-NeRF360 datasets-illustrates that FSGS not only delivers exceptional rendering quality but also achieves an inference speed more than 2000 times faster than existing state-of-the-art methods for sparse-view synthesis. Project webpage: https://zehaozhu.github.io/FSGS/ .