arXiv:2409.07759v1 Announce Type: new
Abstract: 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. To address the aforementioned challenges and enhance streamability, SwinGS integrates spacetime Gaussian with Markov Chain Monte Carlo (MCMC) to adapt the model to fit various 3D scenes across frames, in the meantime employing a sliding window captures Gaussian snapshots for each frame in an accumulative way. We implement a prototype of SwinGS and demonstrate its streamability across various datasets and scenes. Additionally, we develop an interactive WebGL viewer enabling real-time volumetric video playback on most devices with modern browsers, including smartphones and tablets. Experimental results show that SwinGS reduces transmission costs by 83.6% compared to previous work with ignorable compromise in PSNR. Moreover, SwinGS easily scales to long video sequences without compromising quality.
Recent advances in 3D Gaussian Splatting (3DGS) have been revolutionizing the fields of computer vision and computer graphics. The high rendering speed and remarkable quality of 3DGS have made it a popular choice for various applications. However, the application of 3DGS to dynamic scenes has been limited due to challenges such as excessive model sizes, constraints on video duration, and content deviation.
In this paper, the authors introduce SwinGS, a novel framework that solves these challenges and enables real-time streaming of volumetric videos. SwinGS combines spacetime Gaussian with Markov Chain Monte Carlo (MCMC) to adapt the model to fit different 3D scenes across frames. It also uses a sliding window to capture Gaussian snapshots for each frame, accumulating them in a way that enhances streamability.
The multi-disciplinary nature of this framework is worth highlighting. It integrates techniques from computer vision, computer graphics, and probabilistic modeling. The use of MCMC enhances the adaptability of the model, making it suitable for a wide range of dynamic scenes. Additionally, the implementation of a WebGL viewer allows for real-time playback of volumetric videos on various devices.
From the perspective of multimedia information systems, SwinGS offers significant advancements. The ability to stream volumetric videos in real-time opens up possibilities for various applications, such as volumetric video communication, autonomous vehicles, and immersive technologies like virtual, augmented, and mixed reality. These applications heavily rely on the efficient rendering and delivery of multimedia content, and SwinGS addresses this need.
This research also has implications for animations, artificial reality, augmented reality, and virtual realities. The ability to accurately render dynamic scenes in real-time is crucial for creating realistic virtual environments. SwinGS reduces transmission costs compared to previous methods, making it more feasible for applications that require large-scale deployment of volumetric videos. The scalability of SwinGS to long video sequences without compromising quality is crucial for creating immersive experiences that are not limited by the duration of the content.
Overall, SwinGS is a significant contribution to the field of multimedia information systems and related disciplines. Its integration of techniques from various domains, coupled with its ability to address the limitations of previous methods, makes it a promising framework for real-time streaming of volumetric videos in many applications.