用方向场引导动态重建，提升复杂变形的建模精度。
Orientation-anchored Hyper-Gaussian for 4D Reconstruction from Casual Videos
- 引入全局方向场与定向超高斯表示，统一时空几何与运动信息。
- 在真实动态场景中实现优于主流方法的重建保真度。
- 适合需要高精度4D动态重建的视觉应用研究者。
我们提出面向随意拍摄单目视频的高质量4D重建新框架——定向锚定高斯点云（OriGS）。尽管近期工作通过图节点或样条控制点等运动锚点将3D高斯点云扩展至动态场景，但这些方法常依赖低秩假设，在建模无约束动态中固有的复杂区域特异性形变时表现不足。OriGS通过引入基于场景方向的超维表示来解决此问题。首先，估计一个全局方向场，跨时空传播主向前方向，为动态建模提供稳定结构引导。在此基础上，提出面向方向的超高斯，将时间、空间、几何与方向嵌入统一的概率状态。该设计支持通过原则性条件切片推断区域特异性形变，自适应捕捉与全局运动意图一致的多样化局部动态。实验表明，在挑战性真实动态场景中，OriGS的重建保真度显著优于主流方法。
原文摘要 · Abstract (English)
We present Orientation-anchored Gaussian Splatting (OriGS), a novel framework for high-quality 4D reconstruction from casually captured monocular videos. While recent advances extend 3D Gaussian Splatting to dynamic scenes via various motion anchors, such as graph nodes or spline control points, they often rely on low-rank assumptions and fall short in modeling complex, region-specific deformations inherent to unconstrained dynamics. OriGS addresses this by introducing a hyperdimensional representation grounded in scene orientation. We first estimate a Global Orientation Field that propagates principal forward directions across space and time, serving as stable structural guidance for dynamic modeling. Built upon this, we propose Orientation-aware Hyper-Gaussian, a unified formulation that embeds time, space, geometry, and orientation into a coherent probabilistic state. This enables inferring region-specific deformation through principled conditioned slicing, adaptively capturing diverse local dynamics in alignment with global motion intent. Experiments demonstrate the superior reconstruction fidelity of OriGS over mainstream methods in challenging real-world dynamic scenes.
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