QUEEN: QUantized Efficient ENcoding of Dynamic Gaussians for Streaming Free-viewpoint Videos (2412.04469v1)

Abstract: Online free-viewpoint video (FVV) streaming is a challenging problem, which is relatively under-explored. It requires incremental on-the-fly updates to a volumetric representation, fast training and rendering to satisfy real-time constraints and a small memory footprint for efficient transmission. If achieved, it can enhance user experience by enabling novel applications, e.g., 3D video conferencing and live volumetric video broadcast, among others. In this work, we propose a novel framework for QUantized and Efficient ENcoding (QUEEN) for streaming FVV using 3D Gaussian Splatting (3D-GS). QUEEN directly learns Gaussian attribute residuals between consecutive frames at each time-step without imposing any structural constraints on them, allowing for high quality reconstruction and generalizability. To efficiently store the residuals, we further propose a quantization-sparsity framework, which contains a learned latent-decoder for effectively quantizing attribute residuals other than Gaussian positions and a learned gating module to sparsify position residuals. We propose to use the Gaussian viewspace gradient difference vector as a signal to separate the static and dynamic content of the scene. It acts as a guide for effective sparsity learning and speeds up training. On diverse FVV benchmarks, QUEEN outperforms the state-of-the-art online FVV methods on all metrics. Notably, for several highly dynamic scenes, it reduces the model size to just 0.7 MB per frame while training in under 5 sec and rendering at 350 FPS. Project website is at https://research.nvidia.com/labs/amri/projects/queen

• The paper introduces a novel quantization framework using attribute residuals and gating to efficiently encode dynamic Gaussians.
• It achieves state-of-the-art performance with 0.7 MB per frame, under 5 seconds training time, and rendering speeds up to 350 FPS.
• The framework balances model size, speed, and reconstruction quality to advance real-time free-viewpoint video streaming applications.

Overview of "QUEEN: QUantized Efficient ENcoding of Dynamic Gaussians for Streaming Free-viewpoint Videos"

The paper "QUEEN: QUantized Efficient ENcoding of Dynamic Gaussians for Streaming Free-viewpoint Videos" addresses the complex problem of free-viewpoint video (FVV) streaming in an online setting. This task demands real-time updates to a volumetric representation, as well as efficient training and rendering processes to meet stringent memory constraints. The research delineates a novel approach termed QUEEN (Quantized Efficient Encoding for streaming FVV), leveraging 3D Gaussian Splatting (3D-GS), which promises significant improvements over existing methodologies in rendering speed, model size, and reconstruction quality.

Key Contributions

1. Attribute Residuals and Compression: The QUEEN framework pioneers the use of quantized Gaussian attribute residuals between consecutive timeframes, allowing for high-fidelity reconstruction without imposing limiting structural constraints. This framework ingeniously combines a learned latent-decoder for quantizing these attributes with a learned gating module that diligently focuses on sparsifying the position residuals. Here, quantization primarily serves to store non-positional attributes efficiently, whereas gating helps in identifying and preserving only the essential dynamic positional elements.
2. Viewspace Gradient Difference: This concept is utilized to smartly differentiate static and dynamic scene components. It acts as a pivotal guide for effective sparsity learning, thereby improving training efficiency by focusing computational resources on dynamic portions of the scene. This differential targeting significantly aids in achieving higher compression factors without compromising on quality.

Numerical Results

QUEEN outperforms existing state-of-the-art online FVV solutions by reducing the model size to an impressive 0.7 MB per frame. Training times are slashed to under 5 seconds per frame, and rendering speeds achieve up to 350 frames per second (FPS), all while maintaining high reconstruction quality. Notably, in scenarios involving highly dynamic scenes, QUEEN achieves a substantially improved representation efficiency compared to prior methods.

Implications and Future Directions

The immediate practical implication of QUEEN is in areas necessitating real-time 3D scene reconstruction and visualization, such as teleconferencing and immersive media applications like VR and AR. Theoretically, this work suggests a promising direction for future research in streaming 3D video by setting a precedent for blending high compression efficiency with minimal quality loss. Additionally, the methodology can potentially be extended beyond streaming applications to scenarios involving static but otherwise constrained computing environments like mobile AR systems.

Further research is likely to explore refining the gating mechanisms and learning strategies for even more challenging streaming environments, such as those with bandwidth limitations or fewer input views. Moreover, integrating more sophisticated temporal modeling strategies might also enhance dynamic scene adaptability, making such systems even more robust and versatile.

In conclusion, QUEEN appears to be a significant step forward in the field of efficient FVV, providing a robust framework for real-time applications, backed by strong experimental results and a well-conceived technical approach.