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GIFStream: 4D Gaussian-based Immersive Video with Feature Stream

About

Immersive video offers a 6-Dof-free viewing experience, potentially playing a key role in future video technology. Recently, 4D Gaussian Splatting has gained attention as an effective approach for immersive video due to its high rendering efficiency and quality, though maintaining quality with manageable storage remains challenging. To address this, we introduce GIFStream, a novel 4D Gaussian representation using a canonical space and a deformation field enhanced with time-dependent feature streams. These feature streams enable complex motion modeling and allow efficient compression by leveraging temporal correspondence and motion-aware pruning. Additionally, we incorporate both temporal and spatial compression networks for end-to-end compression. Experimental results show that GIFStream delivers high-quality immersive video at 30 Mbps, with real-time rendering and fast decoding on an RTX 4090. Project page: https://xdimlab.github.io/GIFStream

Hao Li, Sicheng Li, Xiang Gao, Abudouaihati Batuer, Lu Yu, Yiyi Liao• 2025

Related benchmarks

TaskDatasetResultRank
Dynamic Scene ReconstructionN3DV (test)
PSNR31.75
32
Novel View SynthesisN3V datasets
PSNR31.75
18
Dynamic 3D ReconstructionN3DV
PSNR (dB)31.75
16
Novel View SynthesisSelfCap (test)
PSNR19.78
9
Novel View SynthesisPackUV-2B (test)
PSNR21.92
9
Novel View SynthesisN3DV (test)
PSNR31.1
9
Novel View SynthesisNeur3D
PSNR31.75
8
Novel View SynthesisPanoptic Sport basketball and boxes
PSNR29.5
7
Dynamic Scene SynthesisSelfCap (900 frames)
PSNR23.8
7
Dynamic Scene SynthesisSelfCap 1200 frames
PSNR24.27
7
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