4DGCPro: Efficient Hierarchical 4D Gaussian Compression for Progressive Volumetric Video Streaming

Zihan Zheng (New York University) · Lan Xu (ShanghaiTech University) · Jiangchao Yao (Shanghai Jiao Tong University) · Xiaoyun Zhang (Shanghai Jiao Tong University) · Wenjun Zhang · Zhenlong Wu (Shanghai Jiaotong University) · Houqiang Zhong · Yuan Tian (Shanghai AI Lab) · Ning Cao (E-surfing Vision Technology Co., Ltd.) · Qiang Hu (Shanghai Jiaotong University)
4d gaussian compressionattribute-specific modelingbitstream generationcompression methodsentropy-optimized trainingmotion-aware groupingperceptually-weighted representationprogressive streamingrate-distortion supervisionrd performancereal-time decodingrenderingscalable detail streamingtemporal redundancyvolumetric video

Achieving seamless viewing of high-fidelity volumetric video, comparable to 2D video experiences, remains an open challenge. Existing volumetric video compression methods either lack the flexibility to adjust quality and bitrate within a single model for efficient streaming across diverse networks and devices, or struggle with real-time decoding and rendering on lightweight mobile platforms. To address these challenges, we introduce 4DGCPro, a novel hierarchical 4D Gaussian compression framework that facilitates real-time mobile decoding and high-quality rendering via progressive volumetric video streaming in a single bitstream. Specifically, we propose a perceptually-weighted and compression-friendly hierarchical 4D Gaussian representation with motion-aware adaptive grouping to reduce temporal redundancy, preserve coherence, and enable scalable multi-level detail streaming. Furthermore, we present an end-to-end entropy-optimized training scheme, which incorporates layer-wise rate-distortion (RD) supervision and attribute-specific entropy modeling for efficient bitstream generation. Extensive experiments show that 4DGCPro enables flexible quality and variable bitrate within a single model, achieving real-time decoding and rendering on mobile devices while outperforming existing methods in RD performance across multiple datasets.