发表机构
Cornell University（康奈尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对单目动态高斯溅射过拟合、复杂场景下表现差的问题，提出SMG模型，引入新数据集，实现了该任务的最优性能。
AI 中文摘要
我们从单目视频出发研究动态高斯溅射。尽管动态高斯溅射的最新进展为动态场景建模提供了有前景的基础，但由于欠约束区域缺乏可靠的正则化信号，它们往往会过拟合到训练视图，并且在遮挡或复杂场景运动下表现不佳。我们提出语义运动图（Semantic Motion Graph, SMG），这是一种将高斯运动建模为低秩语义运动的新方法。我们的核心见解是，现实世界的场景运动通常由语义一致性构建：空间上接近且语义相关的区域往往表现出一致的动力学。为利用这一先验，我们构建SMG以建模场景的结构化运动，高斯运动由SMG节点的运动驱动。我们进一步观察到，高斯运动的不确定性源于优化过程中不可靠的现成先验和弱约束区域，SMG通过使用可靠的图节点引导附近不可靠节点的运动来解决这一问题。为评估具有挑战性的真实场景下的动态高斯溅射，我们引入了一个由自运动-外部相机（ego-exo）设置收集的新多视图数据集。大量实验表明，SMG在具有挑战性的真实基准上的单目动态高斯溅射任务中实现了最先进的性能。项目页面：this https URL。
英文摘要
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/.
CommentsECCV 2026
