Representing Long Volumetric Video with Temporal Gaussian Hierarchy (2412.09608v1)

Abstract: This paper aims to address the challenge of reconstructing long volumetric videos from multi-view RGB videos. Recent dynamic view synthesis methods leverage powerful 4D representations, like feature grids or point cloud sequences, to achieve high-quality rendering results. However, they are typically limited to short (1~2s) video clips and often suffer from large memory footprints when dealing with longer videos. To solve this issue, we propose a novel 4D representation, named Temporal Gaussian Hierarchy, to compactly model long volumetric videos. Our key observation is that there are generally various degrees of temporal redundancy in dynamic scenes, which consist of areas changing at different speeds. Motivated by this, our approach builds a multi-level hierarchy of 4D Gaussian primitives, where each level separately describes scene regions with different degrees of content change, and adaptively shares Gaussian primitives to represent unchanged scene content over different temporal segments, thus effectively reducing the number of Gaussian primitives. In addition, the tree-like structure of the Gaussian hierarchy allows us to efficiently represent the scene at a particular moment with a subset of Gaussian primitives, leading to nearly constant GPU memory usage during the training or rendering regardless of the video length. Extensive experimental results demonstrate the superiority of our method over alternative methods in terms of training cost, rendering speed, and storage usage. To our knowledge, this work is the first approach capable of efficiently handling minutes of volumetric video data while maintaining state-of-the-art rendering quality. Our project page is available at: https://zju3dv.github.io/longvolcap.

• The paper presents a novel Temporal Gaussian Hierarchy that leverages temporal redundancy to efficiently reconstruct long volumetric videos.
• The method structures 4D Gaussian splats hierarchically to adapt to varying motion speeds, achieving 31.79 dB PSNR and 450 FPS at 1080p.
• The approach significantly reduces GPU memory usage, managing up to 18,000-frame sequences within a fixed 17.2 GB VRAM limit.

Representing Long Volumetric Video with Temporal Gaussian Hierarchy

This paper introduces an innovative framework for the representation of long volumetric videos, termed the "Temporal Gaussian Hierarchy" (TGH). The paper addresses the inherent challenges associated with reconstructing extensive volumetric video sequences from multi-view RGB data, a field that has seen significant interest due to its potential applications in augmented and virtual reality (AR/VR), telepresence, and gaming.

Methodology Overview

The Temporal Gaussian Hierarchy framework provides a novel approach to managing the substantial memory and computational demands of prior methodologies. The core idea rests on efficiently leveraging the temporal redundancy present in dynamic scene data. This redundancy manifests as varying motion speeds across different regions and temporal spans within a scene, allowing for a more nuanced representation strategy. By structuring 4D Gaussian splats into a hierarchical format, the method dynamically adjusts the number of primitives necessary to represent scene content at various temporal scales.

In the proposed TGH, each level of the hierarchy handles scene regions exhibiting varied dynamics, essentially adapting to the motion granularity required. The incorporation of multiple temporal segments within this hierarchy ensures that scenes are described with different amounts of detail, flexibly optimizing for slow versus fast-changing areas. This adaptive strategy effectively maintains consistent GPU memory usage across both training and rendering, regardless of video length.

Numerical Results and Performance Metrics

The proposed system demonstrates robust efficiency and scalability, with significant improvements in memory usage and computational costs. Key performance metrics reported include a PSNR of 31.79 dB and a rendering speed of 450 FPS at 1080p on an RTX 4090 GPU, maintaining state-of-the-art visual quality across video lengths previously considered unmanageable by earlier frameworks. The VRAM used is capped at a remarkable 17.2 GB, rather than scaling linearly with video length.

The efficacy of this approach is juxtaposed against prior state-of-the-art methods, such as 4DGS and 4K4D, where TGH manages extensive sequences of up to 18,000 frames—far exceeding the latter's capacity of merely 300 frames before succumbing to GPU memory exhaustion. This scale entails a significant leap in the practical viability of such systems, underpinning its potential in real-time applications where lengthy video sequences are requisite.

Implications and Future Directions

The paper's approach stands at a critical juncture in volumetric video processing, offering profound implications both practically and theoretically. Practically, the capability to efficiently manage memory and computational needs positions this method as a transformative tool in media production, interactive simulations, and AR/VR integration. Its design provides a foundation for real-time processing capabilities, thereby addressing a long-standing bottleneck in volumetric video progress.

Theoretically, this framework sets the stage for exploring further abstractions in Gaussian representations, potentially incorporating more complex dynamic behavior models or improved temporal entanglement strategies. Future research may explore adaptive learning methods which can be seamlessly integrated into hierarchical structures, enhancing granularity and precision in long scenes while minimizing resource footprints.

In conclusion, the Temporal Gaussian Hierarchy emerges as a substantial advancement in volumetric video representation, redefining existing computational constraints while laying the groundwork for future innovation in dynamic scene synthesis. As the framework matures, it will likely inspire further developments, fostering breakthroughs in both its application domain and related computational disciplines.