MeshGAN: Non-linear 3D Morphable Models of Faces

Understand

Generative Adversarial Networks (GANs) are currently the method of choice for generating visual data.

• Certain GAN architectures and training methods have demonstrated exceptional performance in generating realistic synthetic images (in particular, of human faces).
• However, for 3D object, GANs still fall short of the success they have had with images.
• One of the reasons is due to the fact that so far GANs have been applied as 3D convolutional architectures to discrete volumetric representations of 3D objects.