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/.

翻译：建模、理解与重建真实世界在扩展现实/虚拟现实领域至关重要。近期，三维高斯泼溅方法在三维场景建模与理解方面取得了显著成功。类似地，各类四维表征已展现出捕捉四维世界动态变化的能力。然而，目前针对四维表征内部分割的研究尚显不足。本文提出分割任意四维高斯框架，这是首个基于四维高斯实现四维数字世界任意对象分割的框架之一。该框架引入高效的时间身份特征场以处理高斯漂移问题，具备从含噪稀疏输入中学习精确身份特征的潜力。此外，我们提出了四维分割细化流程以消除伪影。我们的框架可在数秒内实现四维高斯表征的精确高质量分割，并展现出移除、重着色、组合及渲染高质量任意对象掩模的能力。更多演示详见：https://jsxzs.github.io/sa4d/。