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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 7 Pith papers
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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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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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ScrewSplat: An End-to-End Method for Articulated Object Recognition
A method that recovers the 3D shape and the rotation or sliding axis of each movable part of an object from RGB video alone, by jointly optimizing randomly initialized screw axes with Gaussian Splatting.
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DRAWER: Digital Reconstruction and Articulation With Environment Realism
A single video of a static indoor scene can be turned into a photorealistic, interactive virtual environment with working articulated objects, usable for games and robot learning.
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MOVIS: Enhancing Multi-Object Novel View Synthesis for Indoor Scenes
MOVIS adds depth and mask conditioning, an auxiliary mask-prediction task, and a timestep curriculum to a view-conditioned diffusion model, improving multi-object novel view synthesis and cross-view consistency.
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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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Advances in Radiance Field for Dynamic Scene: From Neural Field to Gaussian Field
A survey that categorizes dynamic scene reconstruction methods from NeRF to 3D Gaussian splatting into a unified framework based on motion type and representation paradigm.
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