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While recent research has progressively overcome the low-resolutionconstraint of one-shot face video re-enactment with the help of StyleGAN'shigh-fidelity portrait generation, these approaches rely on at least one of thefollowing: explicit 2D/3D priors, optical flow based warping as motiondescriptors, off-the-shelf encoders, etc., which constrain their performance(e.g., inconsistent predictions, inability to capture fine facial details andaccessories, poor generalization, artifacts). We propose an end-to-endframework for simultaneously supporting face attribute edits, facial motionsand deformations, and facial identity control for video generation. It employsa hybrid latent-space that encodes a given frame into a pair of latents:Identity latent, $\mathcal{W}_{ID}$, and Facial deformation latent,$\mathcal{S}_F$, that respectively reside in the $W+$ and $SS$ spaces ofStyleGAN2. Thereby, incorporating the impressive editability-distortiontrade-off of $W+$ and the high disentanglement properties of $SS$. These hybridlatents employ the StyleGAN2 generator to achieve high-fidelity face videore-enactment at $1024^2$. Furthermore, the model supports the generation ofrealistic re-enactment videos with other latent-based semantic edits (e.g.,beard, age, make-up, etc.). Qualitative and quantitative analyses performedagainst state-of-the-art methods demonstrate the superiority of the proposedapproach.

One-Shot Face Video Re-enactment using Hybrid Latent Spaces of StyleGAN2