Building Interactable Replicas of Complex Articulated Objects via
  Gaussian Splatting
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.
67c185f46a31b8fe77434551
Yu Liu
admin_assigned
2025-02-28T12:35:40.098Z
636de85cc4a7a729c164d2b5
/avatars/3e281e547e1697e1c06805e7e63f3918.svg
Yu Liu
user
YuLiu
67c185f46a31b8fe77434552
Baoxiong Jia
admin_assigned
2025-02-28T12:35:33.475Z
6304b389bad6ce7fc02691d5
/avatars/a762ca59624ce409650165f36b973488.svg
Baoxiong Jia
user
BuzzBeater
67c185f46a31b8fe77434553
Ruijie Lu
admin_assigned
2025-02-28T12:35:27.394Z
64ab8cb76324705e6a65f7c4
/avatars/15dcae6c345d31ea6e17c11108a7deb7.svg
Ruijie Lu
user
JasonAplp
67c185f46a31b8fe77434554
Junfeng Ni
admin_assigned
2025-02-28T12:35:20.550Z
65ae5edddacd99fd58277620
https://cdn-avatars.huggingface.co/v1/production/uploads/65ae5edddacd99fd58277620/Yq2adEKtkxaSzdEhCfpnY.jpeg
Junfeng Ni
user
JunfengNi
67c185f46a31b8fe77434555
Song-Chun Zhu
67c185f46a31b8fe77434556
Siyuan Huang
admin_assigned
2025-02-28T12:35:02.710Z
63c7a33121bd95f80ed74652
/avatars/7dd59afea785a2bff0ec2b757abd474e.svg
Siyuan Huang
user
thuhsy
Building Interactable Replicas of Complex Articulated Objects via
  Gaussian Splatting
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.
ArtGS utilizes 3D Gaussians and a skinning-inspired module for improved reconstruction and articulation learning of complex multi-part objects.
3D Gaussians canonical Gaussians coarse-to-fine initialization part-mesh reconstruction part dynamics modeling skinning-inspired joint parameter estimation complex multi-part objects