Control4D: Dynamic Portrait Editing by Learning 4D GAN from 2D
  Diffusion-based Editor
Authors: name: Ruizhi Shao, name: Jingxiang Sun, name: Cheng Peng, name: Zerong Zheng, name: Boyao Zhou, name: Hongwen Zhang, name: Yebin Liu

Recent years have witnessed considerable achievements in editing images with
text instructions. When applying these editors to dynamic scene editing, the
new-style scene tends to be temporally inconsistent due to the frame-by-frame
nature of these 2D editors. To tackle this issue, we propose Control4D, a novel
approach for high-fidelity and temporally consistent 4D portrait editing.
Control4D is built upon an efficient 4D representation with a 2D
diffusion-based editor. Instead of using direct supervisions from the editor,
our method learns a 4D GAN from it and avoids the inconsistent supervision
signals. Specifically, we employ a discriminator to learn the generation
distribution based on the edited images and then update the generator with the
discrimination signals. For more stable training, multi-level information is
extracted from the edited images and used to facilitate the learning of the
generator. Experimental results show that Control4D surpasses previous
approaches and achieves more photo-realistic and consistent 4D editing
performances. The link to our project website is
https://control4darxiv.github.io.