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/.

翻译：动态场景的表示与渲染一直是一项重要但具有挑战性的任务。特别是，为了精确建模复杂运动，通常难以保证高效率。为了实现实时动态场景渲染同时保持高训练与存储效率，我们提出4D高斯溅射（4D-GS）作为动态场景的整体表示方法，而非对每一单独帧应用3D高斯溅射。在4D-GS中，我们提出了一种同时包含3D高斯和4D神经体素的新型显式表示。受HexPlane启发，我们提出了一种分解式神经体素编码算法，用于从4D神经体素高效构建高斯特征，进而通过轻量级MLP预测新时间戳下的高斯形变。我们的4D-GS方法在高分辨率下实现了实时渲染，在RTX 3090 GPU上以800×800分辨率达到82 FPS，同时保持与先前最先进方法相当或更优的质量。更多演示与代码见https://guanjunwu.github.io/4dgs/。