  Modeling, understanding, and reconstructing the real world are crucial in
XR/VR. Recently, 3D Gaussian Splatting (3D-GS) methods have shown remarkable
success in modeling and understanding 3D scenes. Similarly, various 4D
representations have demonstrated the ability to capture the dynamics of the 4D
world. However, there is a dearth of research focusing on segmentation within
4D representations. In this paper, we propose Segment Any 4D Gaussians (SA4D),
one of the first frameworks to segment anything in the 4D digital world based
on 4D Gaussians. In SA4D, an efficient temporal identity feature field is
introduced to handle Gaussian drifting, with the potential to learn precise
identity features from noisy and sparse input. Additionally, a 4D segmentation
refinement process is proposed to remove artifacts. Our SA4D achieves precise,
high-quality segmentation within seconds in 4D Gaussians and shows the ability
to remove, recolor, compose, and render high-quality anything masks. More demos
are available at: https://jsxzs.github.io/sa4d/.
