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

arXiv: https://arxiv.org/abs/1907.10087 · HTML: https://mlsanity.com/paper/1907.10087/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2019.

## Built on by (most cited first, 19 of 19)

- [FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression Recognition in Videos](https://mlsanity.com/paper/2203.09463/) (2022)
- [Sparse to Dense Dynamic 3D Facial Expression Generation](https://mlsanity.com/paper/2105.07463/) (2021)
- [4D Facial Expression Diffusion Model](https://mlsanity.com/paper/2303.16611/) (2023)
- [Reversible Graph Neural Network-based Reaction Distribution Learning for Multiple Appropriate Facial Reactions Generation](https://mlsanity.com/paper/2305.15270/) (2023)
- [Beyond DAGs: A Latent Partial Causal Model for Multimodal Learning](https://mlsanity.com/paper/2402.06223/) (2024)
- [Human Motion Prediction Using Manifold-Aware Wasserstein GAN](https://mlsanity.com/paper/2105.08715/) (2021)
- [Generating Multiple 4D Expression Transitions by Learning Face Landmark Trajectories](https://mlsanity.com/paper/2208.00050/) (2022)
- [AnimateMe: 4D Facial Expressions via Diffusion Models](https://mlsanity.com/paper/2403.17213/) (2024)
- [Graph-Regularized Manifold-Aware Conditional Wasserstein GAN for Brain Functional Connectivity Generation](https://mlsanity.com/paper/2212.05316/) (2022)
- [REACT2023: the first Multi-modal Multiple Appropriate Facial Reaction Generation Challenge](https://mlsanity.com/paper/2306.06583/) (2023)
- [Facial Expression Video Generation Based-On Spatio-temporal Convolutional GAN: FEV-GAN](https://mlsanity.com/paper/2210.11182/) (2022)
- [A Review of Affective Generation Models](https://mlsanity.com/paper/2202.10763/) (2022)
- [3D Skeleton-based Human Motion Prediction with Manifold-Aware GAN](https://mlsanity.com/paper/2203.00736/) (2022)
- [JambaTalk: Speech-Driven 3D Talking Head Generation Based on Hybrid Transformer-Mamba Model](https://mlsanity.com/paper/2408.01627/) (2024)
- [Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection](https://mlsanity.com/paper/2212.05667/) (2022)
- [Autoregressive GAN for Semantic Unconditional Head Motion Generation](https://mlsanity.com/paper/2211.00987/) (2022)
- [FacEnhance: Facial Expression Enhancing with Recurrent DDPMs](https://mlsanity.com/paper/2406.09040/) (2024)
- [Mitigating Knowledge Discrepancies among Multiple Datasets for Task-agnostic Unified Face Alignment](https://mlsanity.com/paper/2503.22359/) (2025)
- [PriorityCut: Occlusion-guided Regularization for Warp-based Image Animation](https://mlsanity.com/paper/2103.11600/) (2021)

## Abstract (arXiv)

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.