GES (Generalized Exponential Splatting) is a novel method for 3D scene modeling that improves upon Gaussian Splatting in terms of memory efficiency and signal representation, particularly for high-frequency signals. The method uses Generalized Exponential Functions (GEF) to model 3D scenes, requiring fewer particles and thus significantly outperforming Gaussian Splatting in efficiency. GES is validated both theoretically and empirically in 1D and 3D scenarios. It is shown to represent signals with sharp edges more accurately, which are challenging for Gaussians due to their low-pass characteristics. Empirical analysis demonstrates that GEF outperforms Gaussians in fitting natural-occurring signals, reducing the need for extensive splitting operations. With a frequency-modulated loss, GES achieves competitive performance in novel-view synthesis benchmarks while requiring less than half the memory storage of Gaussian Splatting and increasing rendering speed by up to 39%. The code is available on the project website. GES is a plug-and-play replacement for Gaussian-based utilities, offering a more efficient and accurate representation of 3D scenes. The method is evaluated on various datasets, showing its effectiveness in novel view synthesis and 3D generation tasks. Results indicate that GES achieves a balance between high fidelity and efficiency, making it a viable option for real-time applications. The method is also applied to fast 3D generation pipelines, demonstrating its effectiveness in reducing memory usage and improving rendering speed. The paper concludes that GES is a promising approach for 3D scene modeling, with potential for further research in complex and dynamic environments.GES (Generalized Exponential Splatting) is a novel method for 3D scene modeling that improves upon Gaussian Splatting in terms of memory efficiency and signal representation, particularly for high-frequency signals. The method uses Generalized Exponential Functions (GEF) to model 3D scenes, requiring fewer particles and thus significantly outperforming Gaussian Splatting in efficiency. GES is validated both theoretically and empirically in 1D and 3D scenarios. It is shown to represent signals with sharp edges more accurately, which are challenging for Gaussians due to their low-pass characteristics. Empirical analysis demonstrates that GEF outperforms Gaussians in fitting natural-occurring signals, reducing the need for extensive splitting operations. With a frequency-modulated loss, GES achieves competitive performance in novel-view synthesis benchmarks while requiring less than half the memory storage of Gaussian Splatting and increasing rendering speed by up to 39%. The code is available on the project website. GES is a plug-and-play replacement for Gaussian-based utilities, offering a more efficient and accurate representation of 3D scenes. The method is evaluated on various datasets, showing its effectiveness in novel view synthesis and 3D generation tasks. Results indicate that GES achieves a balance between high fidelity and efficiency, making it a viable option for real-time applications. The method is also applied to fast 3D generation pipelines, demonstrating its effectiveness in reducing memory usage and improving rendering speed. The paper concludes that GES is a promising approach for 3D scene modeling, with potential for further research in complex and dynamic environments.