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CoGS: Controllable Gaussian Splatting

Heng Yu · Joel Julin · Zoltán Á. Milacski · Koichiro Niinuma · László A. Jeni

Arch 4A-E Poster #200
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Fri 21 Jun 10:30 a.m. PDT — noon PDT


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, 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.

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