Badour AlBahar


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.


Publication Details

Date of publication:
June 21, 2022
Cornell University
Publication note:

Yiran Xu, Badour AlBahar, Jia-Bin Huang: Temporally Consistent Semantic Video Editing. CoRR abs/2206.10590 (2022)