Title: From Tokens to Nodes: Semantic-Guided Motion Control for Dynamic 3D Gaussian Splatting
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: 3d reconstruction, computer vision, monocular dynamic reconstruction

Abstract: Dynamic 3D reconstruction from monocular videos remains difficult due to the ambiguity  inferring 3D motion from limited views and computational demands of modeling temporally varying scenes. While recent sparse control methods alleviate computation by reducing millions of Gaussians to thousands of control points, they suffer from a critical limitation: they allocate points purely by geometry, leading to static redundancy and dynamic insufficiency. We propose a motion-adaptive framework that aligns control density with motion complexity. Leveraging semantic and motion priors from vision foundation models, we establish patch-token-node correspondences and apply motion-adaptive compression to concentrate control points in dynamic regions while suppressing redundancy in static backgrounds. Our approach achieves flexible representational density adaptation through iterative voxelization and motion tendency scoring, directly addressing the fundamental mismatch between control point allocation and motion complexity. To capture temporal evolution, we introduce spline-based trajectory parameterization initialized by 2D tracklets, replacing MLP-based deformation fields to achieve smoother motion representation and more stable optimization. Extensive experiments demonstrate significant improvements in reconstruction quality and efficiency over existing state-of-the-art methods.

TL;DR: We present a motion-adaptive 3D Gaussian Splatting framework that reallocates control points with semantic and motion priors, achieving smoother trajectories and superior dynamic reconstruction.