CoGS: Controllable Gaussian Splatting
Heng Yu Joel Julin Zoltán Á. Milacski Koichiro Niinuma László A. Jeni
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.