3D avatar synthesis

Abstract: The steep growth of video-games is demanding a higher amount of characters in the games. The process of generating characters is very expensive and time consuming. Consequently, this process doesn’t cover the current demands and could be optimized by developing a generative model able to synthesize high- quality 3D avatar faces within minutes. This model would result in drastic gains for gaming companies. Therefore, the aim of this project is to implement a model able to generate realistic 3D avatar faces by generating texture and shape when a limited amount of data is given (<1k samples). This type of model is called 3D Morphable Model and it will also learn the correlation between shape and texture in order to generate consistent results. In order to achieve this final model, which is called joint model, individual models for texture and shape are also developed. The three type of models are built upon StyleGAN2-ADA architecture. The final design of the joint model has three discriminators: a joint discriminator to ensure consistency and two individual discriminators to have good quality for shape and texture. This model was inspired from [1]. The experiments show that the best texture model uses the augmentation techniques introduced in StyleGAN2-ADA. The experiments over the joint model prove that having just one discriminator is not enough to generate good quality results. On the other hand, the joint model with three discriminators give good quality and coherent results. In addition, this joint model outperforms the results of the shape model when training the model with the same number of samples, 969 samples. This model shows a promising path for further improvements.