Temporally Consistent Semantic Video Editing
Abstract
Generative adversarial networks (GANs) have demonstrated impressive image generation quality and semantic editing capability of real images, e.g., changing object classes, modifying attributes, or transferring styles. However, applying these GAN-based editing to a video independently for each frame inevitably results in temporal flickering artifacts. We present a simple yet effective method to facilitate temporally coherent video editing. Our core idea is to minimize the temporal photometric inconsistency by optimizing both the latent code and the pre-trained generator. We evaluate the quality of our editing on different domains and GAN inversion techniques and show favorable results against the baselines.
Cite
Text
Xu et al. "Temporally Consistent Semantic Video Editing." Proceedings of the European Conference on Computer Vision (ECCV), 2022. doi:10.1007/978-3-031-19784-0_21Markdown
[Xu et al. "Temporally Consistent Semantic Video Editing." Proceedings of the European Conference on Computer Vision (ECCV), 2022.](https://mlanthology.org/eccv/2022/xu2022eccv-temporally/) doi:10.1007/978-3-031-19784-0_21BibTeX
@inproceedings{xu2022eccv-temporally,
title = {{Temporally Consistent Semantic Video Editing}},
author = {Xu, Yiran and AlBahar, Badour and Huang, Jia-Bin},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2022},
doi = {10.1007/978-3-031-19784-0_21},
url = {https://mlanthology.org/eccv/2022/xu2022eccv-temporally/}
}