Compressing Streamable Free-Viewpoint Videos to 0.1 MB per Frame

The success of 3D Gaussian Splatting (3DGS) in static scenes has inspired numerous attempts to construct Free-Viewpoint Videos (FVVs) of dynamic scenes from multi-view videos. Despite advancements in current techniques, simultaneously achieving photo-realistic view synthesis results, fast on-the-fly training, real-time rendering, and low storage costs remains a formidable problem. To address these challenges, we propose the first Gaussian-based streamable FVV intelligent compression framework named iFVC. Specifically, we utilize an anchor-based Gaussian representation to model the scene. To achieve on-the-fly training, we propose a Binary Transformation Cache (BTC) to model the dynamic changes between adjacent timesteps, which not only ensures compactness but also supports precise bit rate estimation. Furthermore, we carefully design a high-resolution transformation tri-plane assisted by a saliency grid as our BTC, allowing for accurate dynamic capture. The entire pipeline is regarded as a joint optimization of rate and distortion to achieve optimal compression performance. Experiments on widely used datasets demonstrate the state-of-the-art performance of our framework in both synthesis quality and efficiency, i.e., achieving per-frame training in 13 seconds with a storage cost of 0.1 MB and real-time rendering at 120 FPS.

Authors

• Luyang Tang Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Shenzhen Graduate School, Peking University, China Pengcheng Laboratory, China
• Jiayu Yang Pengcheng Laboratory, China
• Rui Peng Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Shenzhen Graduate School, Peking University, China Pengcheng Laboratory, China
• Yongqi Zhai Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Shenzhen Graduate School, Peking University, China Pengcheng Laboratory, China
• Shihe Shen Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Shenzhen Graduate School, Peking University, China
• Ronggang Wang Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Shenzhen Graduate School, Peking University, China Pengcheng Laboratory, China

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