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A key challenge for novel view synthesis of monocular portrait images is 3Dconsistency under continuous pose variations. Most existing methods rely on 2Dgenerative models which often leads to obvious 3D inconsistency artifacts. Wepresent a 3D-consistent novel view synthesis approach for monocular portraitimages based on a recent proposed 3D-aware GAN, namely Generative RadianceManifolds (GRAM), which has shown strong 3D consistency at multiview imagegeneration of virtual subjects via the radiance manifolds representation.However, simply learning an encoder to map a real image into the latent spaceof GRAM can only reconstruct coarse radiance manifolds without faithful finedetails, while improving the reconstruction fidelity via instance-specificoptimization is time-consuming. We introduce a novel detail manifoldsreconstructor to learn 3D-consistent fine details on the radiance manifoldsfrom monocular images, and combine them with the coarse radiance manifolds forhigh-fidelity reconstruction. The 3D priors derived from the coarse radiancemanifolds are used to regulate the learned details to ensure reasonablesynthesized results at novel views. Trained on in-the-wild 2D images, ourmethod achieves high-fidelity and 3D-consistent portrait synthesis largelyoutperforming the prior art.

Learning Detailed Radiance Manifolds for High-Fidelity and 3D-Consistent Portrait Synthesis from Monocular Image