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A Compact Dynamic 3D Gaussian Representation for Real-Time Dynamic View Synthesis

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3D Gaussian Splatting (3DGS) has shown remarkable success in synthesizing novel views given multiple views of a static scene. Yet, 3DGS faces challenges when applied to dynamic scenes because 3D Gaussian parameters need to be updated per timestep, requiring a large amount of memory and at least a dozen observations per timestep. To address these limitations, we present a compact dynamic 3D Gaussian representation that models positions and rotations as functions of time with a few parameter approximations while keeping other properties of 3DGS including scale, color and opacity invariant. Our method can dramatically reduce memory usage and relax a strict multi-view assumption. In our experiments on monocular and multi-view scenarios, we show that our method not only matches state-of-the-art methods, often linked with slower rendering speeds, in terms of high rendering quality but also significantly surpasses them by achieving a rendering speed of $118$ frames per second (FPS) at a resolution of 1,352$\times$1,014 on a single GPU.

Kai Katsumata, Duc Minh Vo, Hideki Nakayama• 2023

Related benchmarks

TaskDatasetResultRank
Dynamic Scene ReconstructionNeural 3D Video 19 (full)
PSNR30.46
17
Dynamic View SynthesisNeural 3D Video 19 (test)
PSNR30.46
16
Novel View SynthesisD-NeRF (test)
PSNR32.19
5
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