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3D-aware GANs offer new capabilities for view synthesis while preserving theediting functionalities of their 2D counterparts. GAN inversion is a crucialstep that seeks the latent code to reconstruct input images or videos,subsequently enabling diverse editing tasks through manipulation of this latentcode. However, a model pre-trained on a particular dataset (e.g., FFHQ) oftenhas difficulty reconstructing images with out-of-distribution (OOD) objectssuch as faces with heavy make-up or occluding objects. We address this issue byexplicitly modeling OOD objects from the input in 3D-aware GANs. Our core ideais to represent the image using two individual neural radiance fields: one forthe in-distribution content and the other for the out-of-distribution object.The final reconstruction is achieved by optimizing the composition of these tworadiance fields with carefully designed regularization. We demonstrate that ourexplicit decomposition alleviates the inherent trade-off between reconstructionfidelity and editability. We evaluate reconstruction accuracy and editabilityof our method on challenging real face images and videos and showcase favorableresults against other baselines.

In-N-Out: Faithful 3D GAN Inversion with Volumetric Decomposition for Face Editing