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4D Gaussian Splatting for Real-Time Dynamic Scene Rendering

Guanjun Wu · Taoran Yi · Jiemin Fang · Lingxi Xie · Xiaopeng Zhang · Wei Wei · Wenyu Liu · Qi Tian · Xinggang Wang

Arch 4A-E Poster #75
[ ] [ Project Page ]
Fri 21 Jun 10:30 a.m. PDT — noon PDT

Abstract: 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

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