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ArtGS: Building Interactable Replicas of Complex Articulated Objects via Gaussian Splatting
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ArtGS: Building Interactable Replicas of Complex Articulated Objects via Gaussian Splatting
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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. Our work is made publicly available at: https://articulate-gs.github.io.
Forward citations
Cited by 8 Pith papers
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3D Generation for Embodied AI and Robotic Simulation: A Survey
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StructureGS: Structure-aware Gaussian Splatting for Articulated Object Reconstruction
OBB-based part-fitting and contact losses on 3D Gaussians disentangle geometry, appearance, and motion for cleaner articulated reconstruction than photometric-only baselines.
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Revisiting Articulated Parts Perception in Robot Manipulation
Proposes GPS representation for articulated parts, uses VR to annotate 41K frames across 234 objects, trains an RGB-D model, and achieves 73% success in heuristic manipulation policies on 9 objects.
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PokeNet: Learning Kinematic Models of Articulated Objects from Human Observations
PokeNet estimates joint types, axes, ranges, and operation order of articulated objects directly from a single-view point cloud video of a human demonstration.
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Artiverse: A Diverse and Physically Grounded Dataset for Articulated Objects
Artiverse is a new dataset of 5.4K human-authored articulated 3D objects with detailed annotations for parts, multi-DoF joints, interior structures, and physical attributes to enable functional modeling and physics-ba...
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Advances in 4D Representation: Geometry, Motion, and Interaction
A representation-centric survey of 4D generation and reconstruction, organized by geometry, motion, and interaction, with qualitative trade-off comparisons across seven representation families.
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The survey organizes 3D generation for embodied AI into data generators for assets, simulation environments for interaction, and sim-to-real bridges, noting a shift toward interaction readiness and listing bottlenecks...
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3D Generation for Embodied AI and Robotic Simulation: A Survey
The paper surveys 3D generation techniques for embodied AI and robotics, categorizing them into data generation, simulation environments, and sim-to-real bridging while identifying bottlenecks in physical validity and...
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