CoGS: Controllable Gaussian Splatting

• URL: http://arxiv.org/abs/2312.05664v2
• Date: Mon, 22 Apr 2024 17:28:30 GMT
• Title: CoGS: Controllable Gaussian Splatting
• Authors: Heng Yu, Joel Julin, Zoltán Á. Milacski, Koichiro Niinuma, László A. Jeni,
• Abstract summary: Controllable Gaussian Splatting (CoGS) is a new method for capturing and re-animating 3D structures. CoGS offers real-time control of dynamic scenes without the prerequisite of pre-computing control signals. In our evaluations, CoGS consistently outperformed existing dynamic and controllable neural representations in terms of visual fidelity.
• Score: 5.909271640907126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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