Reconstructing dynamic scenes with complex human-object interactions is a fundamental challenge in computer vision and graphics. Existing Gaussian Splatting methods either rely on human pose priors while neglecting dynamic objects, or approximate all motions within a single field, limiting their ability to capture interaction-rich dynamics. To address this gap, we propose Human-Object Interaction Gaussian Splatting (HOIGS), which explicitly models interaction-induced deformation between humans and objects through a cross-attention-based HOI module. Distinct deformation baselines are employed to extract features: HexPlane for humans and Cubic Hermite Spline (CHS) for objects. By integrating these heterogeneous features, HOIGS effectively captures interdependent motions and improves deformation estimation in scenarios involving occlusion, contact, and object manipulation. Comprehensive experiments on multiple datasets demonstrate that our method consistently outperforms state-of-the-art human-centric and 4D Gaussian approaches, highlighting the importance of explicitly modeling human-object interactions for high-fidelity reconstruction.
重构具有复杂人机交互的动态场景是计算机视觉和图形学的一个基本挑战。现有的高斯泼溅方法要么依赖人体姿态先验而忽略动态物体，要么在单一场中近似所有运动，限制了其捕捉交互丰富动态的能力。为了解决这一差距，我们提出了人机交互高斯泼溅（HOIGS），它通过一个基于交叉注意力的HOI模块，显式建模了人与物体之间由交互引起的变形。采用了不同的变形基线来提取特征：对人类使用HexPlane，对物体使用三次埃尔米特样条（CHS）。通过整合这些异构特征，HOIGS有效地捕捉了相互依赖的运动，并改善了涉及遮挡、接触和物体操作场景中的变形估计。在多个数据集上的综合实验表明，我们的方法始终优于最先进的以人为中心和4D高斯方法，突出了显式建模人机交互对于高保真重建的重要性。