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