Make a Face: Towards Arbitrary High Fidelity Face Manipulation
This work addresses the need for more realistic and diverse face manipulation in computer vision, representing an incremental improvement over existing GAN and VAE methods.
The paper tackles the problem of limited resolution and diversity in face manipulation by proposing the Additive Focal Variational Auto-encoder (AF-VAE), which achieves high-fidelity manipulation with state-of-the-art Inception Score and Frechet Inception Distance results.
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.