3D-GOI: 3D GAN Omni-Inversion for Multifaceted and Multi-Object Editing

Abstract

The current GAN inversion methods typically can only edit the appearance and shape of a single object and background while overlooking spatial information. In this work, we propose a 3D editing framework, to enable multifaceted editing of affine information (scale, translation, and rotation) on multiple objects. realizes the complex editing function by inverting the abundance of attribute codes (object shape/ appearance/ scale/ rotation/ translation, background shape/ appearance, and camera pose) controlled by GIRAFFE, a renowned 3D GAN. Accurately inverting all the codes is challenging, 3D-GOI solves this challenge following three main steps. First, we segment the objects and the background in a multi-object image. Second, we use a custom Neural Inversion Encoder to obtain coarse codes of each object. Finally, we use a round-robin optimization algorithm to get precise codes to reconstruct the image. To the best of our knowledge, is the first framework to enable multifaceted editing on multiple objects. Both qualitative and quantitative experiments demonstrate that holds immense potential for flexible, multifaceted editing in complex multi-object scenes. Our project and code are released at https://3d-goi.github.io.

Cite

Text

Li et al. "3D-GOI: 3D GAN Omni-Inversion for Multifaceted and Multi-Object Editing." Proceedings of the European Conference on Computer Vision (ECCV), 2024. doi:10.1007/978-3-031-73033-7_22

Markdown

[Li et al. "3D-GOI: 3D GAN Omni-Inversion for Multifaceted and Multi-Object Editing." Proceedings of the European Conference on Computer Vision (ECCV), 2024.](https://mlanthology.org/eccv/2024/li2024eccv-3dgoi/) doi:10.1007/978-3-031-73033-7_22

BibTeX

@inproceedings{li2024eccv-3dgoi,
  title     = {{3D-GOI: 3D GAN Omni-Inversion for Multifaceted and Multi-Object Editing}},
  author    = {Li, Haoran and Ma, Long and Shi, Haolin and Hao, Yanbin and Liao, Yong and Cheng, Lechao and Zhou, Peng Yuan},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year      = {2024},
  doi       = {10.1007/978-3-031-73033-7_22},
  url       = {https://mlanthology.org/eccv/2024/li2024eccv-3dgoi/}
}