# Face Translation between Images and Videos using Identity-aware CycleGAN

arXiv: https://arxiv.org/abs/1712.00971 · HTML: https://mlsanity.com/paper/1712.00971/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2017.

## Built on by (most cited first, 2 of 2)

- [Identity-Aware CycleGAN for Face Photo-Sketch Synthesis and Recognition](https://mlsanity.com/paper/2103.16019/) (2021)
- [TRAVID: An End-to-End Video Translation Framework](https://mlsanity.com/paper/2309.11338/) (2023)

## Abstract (arXiv)

This paper presents a new problem of unpaired face translation between images and videos, which can be applied to facial video prediction and enhancement. In this problem there exist two major technical challenges: 1) designing a robust translation model between static images and dynamic videos, and 2) preserving facial identity during image-video translation. To address such two problems, we generalize the state-of-the-art image-to-image translation network (Cycle-Consistent Adversarial Networks) to the image-to-video/video-to-image translation context by exploiting a image-video translation model and an identity preservation model. In particular, we apply the state-of-the-art Wasserstein GAN technique to the setting of image-video translation for better convergence, and we meanwhile introduce a face verificator to ensure the identity. Experiments on standard image/video face datasets demonstrate the effectiveness of the proposed model in both terms of qualitative and quantitative evaluations.