SMG: Semantic Motion Graph for Monocular Dynamic Gaussian Splatting

2026-08-31

AI Summary

研究提出Semantic Motion Graph（SMG）方法，通过利用语义一致性来解决单目动态高斯点云渲染中因缺乏可靠正则化信号导致的过拟合和复杂场景运动问题。

Abstract

We study dynamic Gaussian Splatting from monocular videos. While recent advancements in dynamic Gaussian splatting offer a promising foundation for modeling dynamic scenes, they often overfit to the training views and fail under occlusion or complex scene motion due to the lack of reliable regularization signals in under-constrained regions. We propose Semantic Motion Graph (SMG), a novel approach models the Gaussian motion as the low-rank semantic motion. Our key insight is that the real-world scene motion is often structured by semantic coherence: regions that are spatially close and semantically related tend to exhibit consistent dynamics. To leverage this prior, we construct SMG to model structured motion of the scene. The Gaussian motion is driven by the motion of SMG nodes. We further observe that the uncertainty of Gaussian motion arises from both unreliable off-the-shelf priors and weakly constrained regions during optimization. SMG addresses this by using reliable graph nodes to guide the motion of nearby unreliable nodes. To evaluate dynamic Gaussian splatting under challenging real-world scenarios, we introduce a new multiview dataset collected under an ego-exo setup. Extensive experiments demonstrate that SMG achieves state-of-the-art performance on monocular dynamic Gaussian splatting across challenging real-world benchmarks. Project page: https://smg-gaussian.github.io/.

Problem

• dynamic Gaussian splatting
• monocular videos
• occlusion
• complex scene motion
• under-constrained regions

Innovation

• Semantic Motion Graph
• Dynamic Gaussian Splatting
• Monocular Video
• Structured Motion
• Uncertainty Reduction