Dynamic Gaussian-Based Digital Twin Reconstruction of Articulated Multi-Joint Objects

Accurate digital twin reconstruction of articulated objects is essential for graphics-driven simulation, visualization, and embodied applications. However, existing 3D Gaussian Splatting (3DGS) approaches primarily target static scenes and fail to capture the structural dynamics of multi-joint objects, leading to geometric inconsistencies and limited realism. We present a structure-aware reconstruction framework that attaches articulated Gaussian primitives to the kinematic hierarchy, enabling dynamic and editable digital twin reconstruction from multi-view observations via differentiable rendering. By explicitly modeling link-level geometry and motion coupling, the proposed method produces temporally consistent reconstructions that generalize to novel viewpoints and unseen joint configurations. Extensive experiments demonstrate improved visual fidelity, geometric accuracy, and cross-view consistency on articulated robotic systems. Furthermore, the reconstructed digital twins facilitate scalable simulation and enhance sim-to-real policy transfer, highlighting the importance of dynamic Gaussian-based modeling for high-fidelity digital twin applications.