DreamGaussian4D: Generative 4D Gaussian Splatting
Authors: name: Jiawei Ren, name: Liang Pan, name: Jiaxiang Tang, name: Chi Zhang, name: Ang Cao, name: Gang Zeng, name: Ziwei Liu

Remarkable progress has been made in 4D content generation recently. However,
existing methods suffer from long optimization time, lack of motion
controllability, and a low level of detail. In this paper, we introduce
DreamGaussian4D, an efficient 4D generation framework that builds on 4D
Gaussian Splatting representation. Our key insight is that the explicit
modeling of spatial transformations in Gaussian Splatting makes it more
suitable for the 4D generation setting compared with implicit representations.
DreamGaussian4D reduces the optimization time from several hours to just a few
minutes, allows flexible control of the generated 3D motion, and produces
animated meshes that can be efficiently rendered in 3D engines.