LatentSwap: An Efficient Latent Code Mapping Framework for Face Swapping
This work addresses face swapping for image generation applications, presenting an incremental improvement with a simpler and faster training approach.
The paper tackles face swapping by proposing LatentSwap, an efficient framework that generates face swap latent codes using pre-trained models, achieving photorealistic and high-resolution results comparable to other models.
We propose LatentSwap, a simple face swapping framework generating a face swap latent code of a given generator. Utilizing randomly sampled latent codes, our framework is light and does not require datasets besides employing the pre-trained models, with the training procedure also being fast and straightforward. The loss objective consists of only three terms, and can effectively control the face swap results between source and target images. By attaching a pre-trained GAN inversion model independent to the model and using the StyleGAN2 generator, our model produces photorealistic and high-resolution images comparable to other competitive face swap models. We show that our framework is applicable to other generators such as StyleNeRF, paving a way to 3D-aware face swapping and is also compatible with other downstream StyleGAN2 generator tasks. The source code and models can be found at \url{https://github.com/usingcolor/LatentSwap}.