Transforming Facial Weight of Real Images by Editing Latent Space of StyleGAN
This work addresses a domain-specific problem in computer vision for applications like health interventions, but it is incremental as it builds on existing GAN inversion and editing techniques.
The paper tackles the problem of automatically transforming facial weight in real images to look thinner or heavier by developing an invert-and-edit framework that leverages the latent space of StyleGAN, resulting in high-quality and realistic transformations without requiring extensive labeled data.
We present an invert-and-edit framework to automatically transform facial weight of an input face image to look thinner or heavier by leveraging semantic facial attributes encoded in the latent space of Generative Adversarial Networks (GANs). Using a pre-trained StyleGAN as the underlying generator, we first employ an optimization-based embedding method to invert the input image into the StyleGAN latent space. Then, we identify the facial-weight attribute direction in the latent space via supervised learning and edit the inverted latent code by moving it positively or negatively along the extracted feature axis. Our framework is empirically shown to produce high-quality and realistic facial-weight transformations without requiring training GANs with a large amount of labeled face images from scratch. Ultimately, our framework can be utilized as part of an intervention to motivate individuals to make healthier food choices by visualizing the future impacts of their behavior on appearance.