# Temporally Consistent Semantic Video Editing

Source: [Temporally Consistent Semantic Video Editing](<https://www.sota2.com/research/paper/68fa6cd31e209131cea35ff5>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:53.993Z

Authors: Yiran Xu, Badour AlBahar, Jia-Bin Huang

Published: 2022-06-21T17:59:59.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Age Editing on VoxCeleb](<https://www.sota2.com/research/sota/age-editing-on-voxceleb.md>): Age Editing; VoxCeleb; Source Similarity: 0.6004.

- [Angle Editing on VoxCeleb](<https://www.sota2.com/research/sota/angle-editing-on-voxceleb.md>): Angle Editing; VoxCeleb; Similarity to Source: 0.4955.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2206.10590>)

[PDF](<https://arxiv.org/pdf/2206.10590.pdf>)