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.

使用 CoGS 方法可以直接操纵场景元素，实现对动态场景的实时控制，且在视觉保真度方面始终优于现有的动态和可控神经表示方法。