The field of 3D scene reconstruction and dynamic modeling is rapidly advancing, with a focus on developing methods that can accurately capture and predict complex, dynamic scenes. Recent research has centered around improving the accuracy and efficiency of 3D reconstruction techniques, such as Gaussian Splatting, and integrating them with dynamic modeling approaches to better handle moving objects and scenes. Notable advancements include the development of novel frameworks that enable real-time reconstruction of dynamic 3D scenes from monocular videos, as well as methods that can accurately estimate 3D motion and trajectory from 2D tracking sequences. Additionally, researchers have made significant progress in applying these techniques to real-world applications, such as surgical scene reconstruction, robotic navigation, and human motion capture. Overall, the field is moving towards more accurate, efficient, and robust methods for 3D scene reconstruction and dynamic modeling. Noteworthy papers include ODE-GS, which introduces a novel method for dynamic scene extrapolation using latent ODEs, and HAIF-GS, which proposes a unified framework for structured and consistent dynamic modeling using sparse anchor-driven deformation. DynaSplat is also notable for its approach to dynamic-static separation and hierarchical motion modeling for scene reconstruction.