Control4D: Efficient 4D Portrait Editing with Text

Abstract

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.

Cite

Text

Shao et al. "Control4D: Efficient 4D Portrait Editing with Text." Conference on Computer Vision and Pattern Recognition, 2024. doi:10.1109/CVPR52733.2024.00436

Markdown

[Shao et al. "Control4D: Efficient 4D Portrait Editing with Text." Conference on Computer Vision and Pattern Recognition, 2024.](https://mlanthology.org/cvpr/2024/shao2024cvpr-control4d/) doi:10.1109/CVPR52733.2024.00436

BibTeX

@inproceedings{shao2024cvpr-control4d,
  title     = {{Control4D: Efficient 4D Portrait Editing with Text}},
  author    = {Shao, Ruizhi and Sun, Jingxiang and Peng, Cheng and Zheng, Zerong and Zhou, Boyao and Zhang, Hongwen and Liu, Yebin},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2024},
  pages     = {4556-4567},
  doi       = {10.1109/CVPR52733.2024.00436},
  url       = {https://mlanthology.org/cvpr/2024/shao2024cvpr-control4d/}
}