  Representing and rendering dynamic scenes has been an important but
challenging task. Especially, to accurately model complex motions, high
efficiency is usually hard to guarantee. To achieve real-time dynamic scene
rendering while also enjoying high training and storage efficiency, we propose
4D Gaussian Splatting (4D-GS) as a holistic representation for dynamic scenes
rather than applying 3D-GS for each individual frame. In 4D-GS, a novel
explicit representation containing both 3D Gaussians and 4D neural voxels is
proposed. A decomposed neural voxel encoding algorithm inspired by HexPlane is
proposed to efficiently build Gaussian features from 4D neural voxels and then
a lightweight MLP is applied to predict Gaussian deformations at novel
timestamps. Our 4D-GS method achieves real-time rendering under high
resolutions, 82 FPS at an 800$\times$800 resolution on an RTX 3090 GPU while
maintaining comparable or better quality than previous state-of-the-art
methods. More demos and code are available at
https://guanjunwu.github.io/4dgs/.
