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3D GANs have the ability to generate latent codes for entire 3D volumesrather than only 2D images. These models offer desirable features likehigh-quality geometry and multi-view consistency, but, unlike their 2Dcounterparts, complex semantic image editing tasks for 3D GANs have only beenpartially explored. To address this problem, we propose LatentSwap3D, asemantic edit approach based on latent space discovery that can be used withany off-the-shelf 3D or 2D GAN model and on any dataset. LatentSwap3D relies onidentifying the latent code dimensions corresponding to specific attributes byfeature ranking using a random forest classifier. It then performs the edit byswapping the selected dimensions of the image being edited with the ones froman automatically selected reference image. Compared to other latent spacecontrol-based edit methods, which were mainly designed for 2D GANs, our methodon 3D GANs provides remarkably consistent semantic edits in a disentangledmanner and outperforms others both qualitatively and quantitatively. We showresults on seven 3D GANs (pi-GAN, GIRAFFE, StyleSDF, MVCGAN, EG3D, StyleNeRF,and VolumeGAN) and on five datasets (FFHQ, AFHQ, Cats, MetFaces, and CompCars).

LatentSwap3D: Semantic Edits on 3D Image GANs