I studied Neural Networks for texture generation. In particular, I have researched and created Generative Adversarial Networks (GANs) for rendering new images of different types of ground terrain textures (such as soil, grass plains, drought plains, etc). A GAN consists of two neural networks: a discriminator and a generator. The purpose of the discriminator is to distinguish between the real image and fake image by taking the inputs from a set of real images and a set of fake images from the generator. On the other hand, the generator creates images with random pixels. There has been ample research done with GANs that produce synthetic 2D images or 3D graphical models. The GAN I have created, given a set of input images, will dynamically render a set of different variations of original images of terrain textures. For example, if the training input is a set of green grass, the output will be a set of withered grass plain, fresh grass plain, and so on. I sent out a survey to the campus to evaluate these images and my final results tend out to be similar to the original textures of ground terrain.