PGAN: Prediction Generative Adversarial Nets for Meshes Unlike images, the topology similarity among meshes can hardly be handled with traditional signal processing tools because of their irregular structures. Geometry image parameterization provides a way to represent 3D meshes in the form of 2D geometry and normal images. However, most existing methods, including the CoGAN are not suitable for such unnatural images corresponding to meshes. To solve this problem, we propose a Prediction Generative Adversarial Network (PGAN) to learn a joint distribution of geometry and normal images for generating meshes. Particularly, we enforce a prediction constraint on the geometry GAN and normal GAN in our PGAN utilizing the inherent relationship between the geometry and normal. The experimental results on face mesh generation indicate that our PGAN outperforms in generating realistic face models with rich facial attributes such as facial expression and retaining the geometry of the faces.

3DMaskGAN:Unsupervised SingleView 3D Object Reconstruction 3D object reconstruction has always been a hot topic in computer vision. Especially in recent years, many methods of learning volumetric predictions achieve robust 3D reconstruction using deep networks with 3D convolutional operations, which are direct analogies to classical 2D ones. However, the majority of methods employ strong shape priors and exist computationally waste in predicting 3D shapes. In this paper, we propose 3DMaskGAN, a novel framework to efficiently accomplish the task of unsupervised singleview 3D object reconstruction. We use 3D Generative Adversarial Networks (GAN) to predict the 3D shape from the singleview image and improve reconstruction accuracy by applying 2D projection masks instead of 3D priors simultaneously. The key idea is to insert a projector (a buildin camera system to approximate the true rendering pipeline) into the framework of 3D GAN to synthesize novel masks for optimization. We learn singleclass and multiclass objects to evaluate our network. Experimental results show that our framework achieves impressive performance with fewer training iterations in terms of unsupervised shape predictions.