RigGS: Rigging of 3D Gaussians for Modeling Articulated Objects in Videos

• URL: http://arxiv.org/abs/2503.16822v1
• Date: Fri, 21 Mar 2025 03:27:07 GMT
• Title: RigGS: Rigging of 3D Gaussians for Modeling Articulated Objects in Videos
• Authors: Yuxin Yao, Zhi Deng, Junhui Hou,
• Abstract summary: RigGS is a new paradigm that leverages 3D Gaussian representation and skeleton-based motion representation to model dynamic objects.<n>Our method can generate realistic new actions easily for objects and achieve high-quality rendering.
• Score: 50.37136267234771
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper considers the problem of modeling articulated objects captured in 2D videos to enable novel view synthesis, while also being easily editable, drivable, and re-posable. To tackle this challenging problem, we propose RigGS, a new paradigm that leverages 3D Gaussian representation and skeleton-based motion representation to model dynamic objects without utilizing additional template priors. Specifically, we first propose skeleton-aware node-controlled deformation, which deforms a canonical 3D Gaussian representation over time to initialize the modeling process, producing candidate skeleton nodes that are further simplified into a sparse 3D skeleton according to their motion and semantic information. Subsequently, based on the resulting skeleton, we design learnable skin deformations and pose-dependent detailed deformations, thereby easily deforming the 3D Gaussian representation to generate new actions and render further high-quality images from novel views. Extensive experiments demonstrate that our method can generate realistic new actions easily for objects and achieve high-quality rendering.

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