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

Description

Current Generative Adversarial Networks (GANs) produce photorealisticrenderings of portrait images. Embedding real images into the latent spaceof such models enables high-level image editing. While recent methodsprovide considerable semantic control over the (re-)generated images, theycan only generate a limited set of viewpoints and cannot explicitly controlthe camera. Such 3D camera control is required for 3D virtual and mixedreality applications. In our solution, we use a few images of a face to perform3D reconstruction, and we introduce the notion of the GAN camera manifold,the key element allowing us to precisely define the range of images that theGAN can reproduce in a stable manner. We train a small face-specific neuralimplicit representation network to map a captured face to this manifoldand complement it with a warping scheme to obtain free-viewpoint novel-view synthesis. We show how our approach ś due to its precise cameracontrol ś enables the integration of a pre-trained StyleGAN into standard 3Drendering pipelines, allowing e.g., stereo rendering or consistent insertionof faces in synthetic 3D environments. Our solution proposes the first trulyfree-viewpoint rendering of realistic faces at interactive rates, using onlya small number of casual photos as input, while simultaneously allowingsemantic editing capabilities, such as facial expression or lighting changes.

Abstract

International audience

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