MrNeRF (@janusch_patas)
2025-05-17 | โค๏ธ 185 | ๐ 31
MoDGS: Dynamic Gaussian Splatting from Casually-captured Monocular Videos with Depth Priors
Abstract: In this paper, we propose MoDGS, a new pipeline to render novel views of dynamic scenes from a casually captured monocular video.
Previous monocular dynamic NeRF or Gaussian Splatting methods strongly rely on the rapid movement of input cameras to construct multiview consistency but struggle to reconstruct dynamic scenes on casually captured input videos where cameras are either static or move slowly.
To address this challenging task, MoDGS adopts recent single-view depth estimation methods to guide the learning of the dynamic scene. Then, a novel 3D-aware initialization method is proposed to learn a reasonable deformation field, and a new robust depth loss is introduced to guide the learning of dynamic scene geometry.
Comprehensive experiments demonstrate that MoDGS is able to render high-quality novel view images of dynamic scenes from just a casually captured monocular video, outperforming state-of-the-art methods by a significant margin.
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