Facial expression video synthesis from the StyleGAN latent space

Lei Zhang; Chris Pollett

doi:10.1117/12.2599392

30 June 2021 Facial expression video synthesis from the StyleGAN latent space

Lei Zhang, Chris Pollett

Proceedings Volume 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021); 118781M (2021) https://doi.org/10.1117/12.2599392
Event: Thirteenth International Conference on Digital Image Processing, 2021, Singapore, Singapore

Abstract

Generative neural network models have been used to create impressive synthetic images. However, artificial video synthesis is still hard, even for these models. The best videos that generative models can currently create are a few seconds long, distorted, and low resolution. We propose and implement a model to synthesize videos at 1024 × 1024 × 32 resolution that include human facial expressions by using static images generated from a Generative Adversarial Network trained on human facial images. To the best of our knowledge, this is the first work that generates realistic videos that are larger than 256 × 256 resolution from single starting images. Our model improves video synthesis in both quantitative and qualitative ways as compared to two state-of-the-art models: TGAN and MocoGAN. In a quantitative comparison, we achieve a best Average Content Distance (ACD) score of 0.167, as compared to 0.305 and 0.201 for TGAN and MocoGAN, respectively

Citation Download Citation

Lei Zhang and Chris Pollett "Facial expression video synthesis from the StyleGAN latent space", Proc. SPIE 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021), 118781M (30 June 2021); https://doi.org/10.1117/12.2599392

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