# Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets

## TL;DR

This paper introduces a novel manifold-valued Wasserstein GAN approach on the hypersphere for generating realistic, dynamic facial expression videos from neutral faces, leveraging facial geometry and landmark motion modeling.

## Contribution

It is the first to utilize manifold-valued representations with GANs for dynamic facial expression generation, improving realism and identity preservation.

## Key findings

- Effective in generating realistic facial expression videos
- Outperforms existing methods in expression transfer and data augmentation
- Validated on Oulu-CASIA and MUG datasets

## Abstract

In this work, we propose a novel approach for generating videos of the six basic facial expressions given a neutral face image. We propose to exploit the face geometry by modeling the facial landmarks motion as curves encoded as points on a hypersphere. By proposing a conditional version of manifold-valued Wasserstein generative adversarial network (GAN) for motion generation on the hypersphere, we learn the distribution of facial expression dynamics of different classes, from which we synthesize new facial expression motions. The resulting motions can be transformed to sequences of landmarks and then to images sequences by editing the texture information using another conditional Generative Adversarial Network. To the best of our knowledge, this is the first work that explores manifold-valued representations with GAN to address the problem of dynamic facial expression generation. We evaluate our proposed approach both quantitatively and qualitatively on two public datasets; Oulu-CASIA and MUG Facial Expression. Our experimental results demonstrate the effectiveness of our approach in generating realistic videos with continuous motion, realistic appearance and identity preservation. We also show the efficiency of our framework for dynamic facial expressions generation, dynamic facial expression transfer and data augmentation for training improved emotion recognition models.