FreeStyleGAN: Free-view Editable Portrait Rendering with the Camera Manifold

TL;DR

FreeStyleGAN introduces a method for photorealistic, editable 3D portrait rendering from casual photos, enabling precise camera control, semantic editing, and integration into 3D pipelines.

Contribution

It develops the concept of a GAN camera manifold and a face-specific neural implicit representation for free-viewpoint face synthesis from limited images.

Findings

Enables stable, free-viewpoint face rendering from few casual photos.

Allows semantic editing like expression and lighting adjustments.

Integrates with 3D pipelines for stereo and virtual reality applications.

Abstract

Current Generative Adversarial Networks (GANs) produce photorealistic renderings of portrait images. Embedding real images into the latent space of such models enables high-level image editing. While recent methods provide considerable semantic control over the (re-)generated images, they can only generate a limited set of viewpoints and cannot explicitly control the camera. Such 3D camera control is required for 3D virtual and mixed reality applications. In our solution, we use a few images of a face to perform 3D reconstruction, and we introduce the notion of the GAN camera manifold, the key element allowing us to precisely define the range of images that the GAN can reproduce in a stable manner. We train a small face-specific neural implicit representation network to map a captured face to this manifold and complement it with a warping scheme to obtain free-viewpoint novel-view synthesis. We show how our approach - due to its precise camera control - enables the integration of a pre-trained StyleGAN into standard 3D rendering pipelines, allowing e.g., stereo rendering or consistent insertion of faces in synthetic 3D environments. Our solution proposes the first truly free-viewpoint rendering of realistic faces at interactive rates, using only a small number of casual photos as input, while simultaneously allowing semantic editing capabilities, such as facial expression or lighting changes.