Geometry Sampling for 3D Face Generation via DCGAN

Despite numerous progresses in the past decades, 3D shape acquisition techniques remain a threshold for various 3D face based applications. Moreover, advanced 2D data generative models based on the deep networks may not be directly applicable for 3D objects. In this work, we propose a geometry sampling approach to bridge the gap between unstructured 3D face models and the powerful deep networks towards an unsupervised 3D face generative model. Specifically, we devise a geometry sampling approach to obtain a structured representation of 3D faces, which enable us to adapt the 3D faces to the Deep Convolution Generative Adversarial Network (DCGAN) for 3D face generation. We have demonstrated the effectiveness of our generative model by producing a large variety of 3D faces with different facial expressions.