Reconstructing and simulating elastic objects from visual observations is crucial for applications in computer vision and robotics. Existing methods, such as 3D Gaussians, model 3D appearance and geometry but lack the ability to estimate and simulate physical properties. The core challenge lies in integrating an expressive yet efficient physical dynamics model. The authors propose Spring-Gaus, a 3D physical object representation that integrates a 3D Spring-Mass model into 3D Gaussian kernels. This enables the reconstruction of the visual appearance, shape, and physical dynamics of elastic objects from videos of multiple viewpoints. Spring-Gaus is designed to be expressive and efficient, allowing for future predictions and simulations under various initial states and environmental conditions. The approach is evaluated on both synthetic and real-world datasets, demonstrating accurate reconstruction and simulation of elastic objects. Key contributions include the integration of a 3D Spring-Mass model, a decoupled reconstruction pipeline, and effective optimization strategies. The method outperforms existing approaches in terms of geometric accuracy, appearance preservation, and future prediction capabilities.Reconstructing and simulating elastic objects from visual observations is crucial for applications in computer vision and robotics. Existing methods, such as 3D Gaussians, model 3D appearance and geometry but lack the ability to estimate and simulate physical properties. The core challenge lies in integrating an expressive yet efficient physical dynamics model. The authors propose Spring-Gaus, a 3D physical object representation that integrates a 3D Spring-Mass model into 3D Gaussian kernels. This enables the reconstruction of the visual appearance, shape, and physical dynamics of elastic objects from videos of multiple viewpoints. Spring-Gaus is designed to be expressive and efficient, allowing for future predictions and simulations under various initial states and environmental conditions. The approach is evaluated on both synthetic and real-world datasets, demonstrating accurate reconstruction and simulation of elastic objects. Key contributions include the integration of a 3D Spring-Mass model, a decoupled reconstruction pipeline, and effective optimization strategies. The method outperforms existing approaches in terms of geometric accuracy, appearance preservation, and future prediction capabilities.