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.

[ArXiv] [Paper] [Code (Coming soon)]

Fig 2. Pipeline of Control4D. Our method first utilizes GaussianPlanes to train the implicit representation of a 4D portrait scene, which are then rendered into latent features and RGB images using Gaussian rendering, serving as inputs for the GAN-based generator. Meanwhile, we apply the 2D-diffusion-based editor to edit the dataset with the noisy results and conditions as inputs, leading to updated results that are used as real images while the Superres. Module’s outputs serve as fake images fed into the Discriminator for discrimination. The discriminative results are used to calculate loss, allowing for iterative updates of both the Generator and Discriminator.