  We introduce Control4D, an innovative framework for editing dynamic 4D
portraits using text instructions. Our method addresses the prevalent
challenges in 4D editing, notably the inefficiencies of existing 4D
representations and the inconsistent editing effect caused by diffusion-based
editors. We first propose GaussianPlanes, a novel 4D representation that makes
Gaussian Splatting more structured by applying plane-based decomposition in 3D
space and time. This enhances both efficiency and robustness in 4D editing.
Furthermore, we propose to leverage a 4D generator to learn a more continuous
generation space from inconsistent edited images produced by the
diffusion-based editor, which effectively improves the consistency and quality
of 4D editing. Comprehensive evaluation demonstrates the superiority of
Control4D, including significantly reduced training time, high-quality
rendering, and spatial-temporal consistency in 4D portrait editing. The link to
our project website is https://control4darxiv.github.io.
