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SkeletonGaussian: Editable 4D generation through Gaussian skeletonization
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AI
DOI：10.26599/cvm.2026.9450557.png)
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Abstract
En 中文
4D generation has made remarkable progress in synthesizing dynamic 3D objects from input text, images, or video. However, existing methods often represent motion as an implicit deformation field, which limits direct control and editability. To address this, we propose SkeletonGaussian, a novel framework for generating editable, dynamic 3D Gaussians from monocular video input. Our approach introduces a hierarchical, articulated representation that decomposes motion into sparse, rigid motion explicitly driven by a skeleton and fine-grained, non-rigid motion. In detail, we extract a robust skeleton and drive rigid motion via linear blend skinning, followed by hexplane based refinement for non-rigid deformation, which enhances interpretability and editability. Experimental results show that SkeletonGaussian surpasses existing methods in visual quality while enabling more intuitive articulated motion editing, establishing a new paradigm for controllable 4D generation.
Keywords:
4D generation
Gaussian splatting
motion editing
skeleton modeling
dynamic 3D
Journal
IF:
18.3
Papers:
310
Citations:
2.6K