FaceFeat-GAN: a Two-Stage Approach for Identity-Preserving Face Synthesis
This work addresses identity preservation and diversity in face synthesis for applications like facial recognition and entertainment, representing an incremental improvement over existing GAN-based approaches.
The paper tackled the problem of synthesizing face images that preserve facial identity while maintaining high diversity, and introduced FaceFeat-GAN, a two-stage generative model that significantly outperforms previous methods in image quality and diversity.
The advance of Generative Adversarial Networks (GANs) enables realistic face image synthesis. However, synthesizing face images that preserve facial identity as well as have high diversity within each identity remains challenging. To address this problem, we present FaceFeat-GAN, a novel generative model that improves both image quality and diversity by using two stages. Unlike existing single-stage models that map random noise to image directly, our two-stage synthesis includes the first stage of diverse feature generation and the second stage of feature-to-image rendering. The competitions between generators and discriminators are carefully designed in both stages with different objective functions. Specially, in the first stage, they compete in the feature domain to synthesize various facial features rather than images. In the second stage, they compete in the image domain to render photo-realistic images that contain high diversity but preserve identity. Extensive experiments show that FaceFeat-GAN generates images that not only retain identity information but also have high diversity and quality, significantly outperforming previous methods.