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Current Generative Adversarial Networks (GANs) produce photorealisticrenderings of portrait images. Embedding real images into the latent space ofsuch models enables high-level image editing. While recent methods provideconsiderable semantic control over the (re-)generated images, they can onlygenerate a limited set of viewpoints and cannot explicitly control the camera.Such 3D camera control is required for 3D virtual and mixed realityapplications. In our solution, we use a few images of a face to perform 3Dreconstruction, and we introduce the notion of the GAN camera manifold, the keyelement allowing us to precisely define the range of images that the GAN canreproduce in a stable manner. We train a small face-specific neural implicitrepresentation network to map a captured face to this manifold and complementit with a warping scheme to obtain free-viewpoint novel-view synthesis. We showhow our approach - due to its precise camera control - enables the integrationof 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 realisticfaces at interactive rates, using only a small number of casual photos asinput, while simultaneously allowing semantic editing capabilities, such asfacial expression or lighting changes.

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