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- W4312853918 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." @default.
- W4312853918 created "2023-01-05" @default.
- W4312853918 creator A5031919120 @default.
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- W4312853918 date "2022-01-01" @default.
- W4312853918 modified "2023-10-15" @default.
- W4312853918 title "Temporally Consistent Semantic Video Editing" @default.
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- W4312853918 doi "https://doi.org/10.1007/978-3-031-19784-0_21" @default.
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