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.
- 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)