CoGS：可控的高斯泼溅
CoGS: Controllable Gaussian Splatting
AI总结：
本文提出CoGS方法，通过直接操纵场景元素实现动态场景的实时控制，无需预计算控制信号，在视觉保真度上优于现有动态神经表示。
摘要：
捕获并重新驱动铰接对象的3D结构面临重大障碍。一方面，需要大量校准的多视角设置的方法过于复杂且资源密集，限制了其实际适用性。另一方面，虽然单相机Neural Radiance Fields (NeRFs)提供了更简化的方法，但其训练和渲染成本过高。3D Gaussian Splatting本是一个合适的替代方案，但存在两个问题：首先，现有的3D动态高斯方法需要同步的多视角相机；其次，动态场景中缺乏可控性。我们提出了CoGS，一种用于可控高斯泼溅的方法，能够直接操纵场景元素，提供对动态场景的实时控制，而无需预先计算控制信号。我们使用包含不同难度动态对象的合成和真实世界数据集评估了CoGS。在评估中，CoGS在视觉保真度方面始终优于现有的动态和可控神经表示。
英文摘要：
Capturing and re-animating the 3D structure of articulated objects present significant barriers. On one hand, methods requiring extensively calibrated multi-view setups are prohibitively complex and resource-intensive, limiting their practical applicability. On the other hand, while single-camera Neural Radiance Fields (NeRFs) offer a more streamlined approach, they have excessive training and rendering costs. 3D Gaussian Splatting would be a suitable alternative but for two reasons. Firstly, existing methods for 3D dynamic Gaussians require synchronized multi-view cameras, and secondly, the lack of controllability in dynamic scenarios. We present CoGS, a method for Controllable Gaussian Splatting, that enables the direct manipulation of scene elements, offering real-time control of dynamic scenes without the prerequisite of pre-computing control signals. We evaluated CoGS using both synthetic and real-world datasets that include dynamic objects that differ in degree of difficulty. In our evaluations, CoGS consistently outperformed existing dynamic and controllable neural representations in terms of visual fidelity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
