SwinGS: Sliding Window Gaussian Splatting for Volumetric Video Streaming with Arbitrary Length

Understand

Recent advances in 3D Gaussian Splatting (3DGS) have garnered significant attention in computer vision and computer graphics due to its high rendering speed and remarkable quality.

• While extant research has endeavored to extend the application of 3DGS from static to dynamic scenes, such efforts have been consistently impeded by excessive model sizes, constraints on video duration, and content deviation.
• These limitations significantly compromise the streamability of dynamic 3D Gaussian models, thereby restricting their utility in downstream applications, including volumetric video, autonomous vehicle, and immersive technologies such as virtual, augmented, and mixed reality.
• This paper introduces SwinGS, a novel framework for training, delivering, and rendering volumetric video in a real-time streaming fashion.