High-fidelity interactive digital assets are essential for embodied intelligence and robotic interaction, yet articulated objects remain challenging to reconstruct due to their complex structures and coupled geometry-motion relationships. Existing methods suffer from instability in geometry-motion joint optimization, while their generalization remains limited on complex multi-joint or out-of-distribution objects. To address these challenges, we propose GEAR, an EM-style alternating optimization framework that jointly models geometry and motion as interdependent components within a Gaussian Splatting representation. GEAR treats part segmentation as a latent variable and joint motion parameters as explicit variables, alternately refining them for improved convergence and geometric-motion consistency. To enhance part segmentation quality without sacrificing generalization, we leverage a vanilla 2D segmentation model to provide multi-view part priors, and employ a weakly supervised constraint to regularize the latent variable. Experiments on multiple benchmarks and our newly constructed dataset GEAR-Multi demonstrate that GEAR achieves state-of-the-art results in geometric reconstruction and motion parameters estimation, particularly on complex articulated objects with multiple movable parts.

翻译：摘要：高保真交互式数字资产对于具身智能与机器人交互至关重要，但铰接物体因其复杂结构及几何-运动耦合关系，在重建过程中仍面临挑战。现有方法在几何-运动联合优化中存在不稳定性，且针对复杂多关节或分布外物体的泛化能力有限。为解决上述问题，我们提出GEAR——一种基于期望最大化（EM）的交替优化框架，将几何与运动作为高斯溅射表示中的相互依赖组件进行联合建模。GEAR将部件分割视为隐变量，关节运动参数视为显式变量，通过交替优化提升收敛性与几何-运动一致性。为在不牺牲泛化能力的前提下提升部件分割质量，我们利用基础二维分割模型提供多视角部件先验，并引入弱监督约束对隐变量进行正则化。在多个基准数据集及我们新构建的GEAR-Multi数据集上的实验表明，GEAR在几何重建与运动参数估计方面达到最优性能，尤其适用于具有多个运动部件的复杂铰接物体。