  Recent studies have shown remarkable success in face manipulation task with
the advance of GANs and VAEs paradigms, but the outputs are sometimes limited
to low-resolution and lack of diversity.
  In this work, we propose Additive Focal Variational Auto-encoder (AF-VAE), a
novel approach that can arbitrarily manipulate high-resolution face images
using a simple yet effective model and only weak supervision of reconstruction
and KL divergence losses. First, a novel additive Gaussian Mixture assumption
is introduced with an unsupervised clustering mechanism in the structural
latent space, which endows better disentanglement and boosts multi-modal
representation with external memory. Second, to improve the perceptual quality
of synthesized results, two simple strategies in architecture design are
further tailored and discussed on the behavior of Human Visual System (HVS) for
the first time, allowing for fine control over the model complexity and sample
quality. Human opinion studies and new state-of-the-art Inception Score (IS) /
Frechet Inception Distance (FID) demonstrate the superiority of our approach
over existing algorithms, advancing both the fidelity and extremity of face
manipulation task.
