Building Interactable Replicas of Complex Articulated Objects via
  Gaussian Splatting
Authors: name: Yu Liu, name: Baoxiong Jia, name: Ruijie Lu, name: Junfeng Ni, name: Song-Chun Zhu, name: Siyuan Huang

Building articulated objects is a key challenge in computer vision. Existing
methods often fail to effectively integrate information across different object
states, limiting the accuracy of part-mesh reconstruction and part dynamics
modeling, particularly for complex multi-part articulated objects. We introduce
ArtGS, a novel approach that leverages 3D Gaussians as a flexible and efficient
representation to address these issues. Our method incorporates canonical
Gaussians with coarse-to-fine initialization and updates for aligning
articulated part information across different object states, and employs a
skinning-inspired part dynamics modeling module to improve both part-mesh
reconstruction and articulation learning. Extensive experiments on both
synthetic and real-world datasets, including a new benchmark for complex
multi-part objects, demonstrate that ArtGS achieves state-of-the-art
performance in joint parameter estimation and part mesh reconstruction. Our
approach significantly improves reconstruction quality and efficiency,
especially for multi-part articulated objects. Additionally, we provide
comprehensive analyses of our design choices, validating the effectiveness of
each component to highlight potential areas for future improvement.