Light Field Synthesis from a Single Image using Improved Wasserstein Generative Adversarial Network

Figure 1: Vector field visualizations synthesized using MRF texture synthesis with a gradient example texture that is rotated and scaled according to the vector field.. The two

After synthesizing a new height field with similar statistical prop- erties using standard 2D texture synthesis methods, they ren- der a gray image given a desired view and

Interior light probes are acquired from a real scene, then used to shoot light onto the virtual scene geometry to accelerate image synthesis by assuming the light sources shot act

We consider the light field synthesis problem equivalent to image super-resolution, and solve it by using the improved Wasserstein Generative Adversarial Network with gradient

Figure 1: Given an input exemplar and a target portrait photo, we can generate stylized output with comparable or superior visual quality as compared to several state-of-the-art

In this paper, we develop a Virtual Try-on Generative Adversarial Network (VITON-GAN), that generates virtual try-on images using images of in-shop clothing and a model person..

We develop a Virtual Try-on Generative Adversarial Network (VITON-GAN), that generates virtual try-on images from images of in-shop clothing and a model person. VITON-GAN enhances

For DCGAN and WGAN, respectively, we generated 40 random samples from the generator and drew 40 random sam- ples of real data for each benign, malignant and healthy patchesc. In