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arXiv · 2512.11356

Prior-Enhanced Gaussian Splatting for Dynamic Scene Reconstruction from Casual Video

Meng-Li Shih·Ying-Huan Chen·Yu-Lun Liu·Brian Curless
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Abstract

We introduce a fully automatic pipeline for dynamic scene reconstruction from casually captured monocular RGB videos. Rather than designing a new scene representation, we enhance the priors that drive Dynamic Gaussian Splatting. Video segmentation combined with epipolar-error maps yields object-level masks that closely follow thin structures; these masks (i) guide an object-depth loss that sharpens the consistent video depth, and (ii) support skeleton-based sampling plus mask-guided re-identification to produce reliable, comprehensive 2-D tracks. Two additional objectives embed the refined priors in the reconstruction stage: a virtual-view depth loss removes floaters, and a scaffold-projection loss ties motion nodes to the tracks, preserving fine geometry and coherent motion. The resulting system surpasses previous monocular dynamic scene reconstruction methods and delivers visibly superior renderings

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Meng-Li Shih, Ying-Huan Chen, Yu-Lun Liu, Brian Curless. 2025-12-12. Prior-Enhanced Gaussian Splatting for Dynamic Scene Reconstruction from Casual Video. https://arxiv.org/abs/2512.11356

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