Synthesizing larger texture images from a smaller exemplar is an important task in graphics and vision. The conventional CNNs, recently adopted for synthesis, require to train and test on the same set of images and fail to generalize to unseen images. This is mainly because those CNNs fully rely on convolutional and upsampling layers that operate locally and not suitable for a task as global as texture synthesis.

First-person shooters (FPS) games are dominant in the competitive gaming and esports community. However, relatively few tools are available for experimenters interested in studying mechanics of these games in a controlled, repeatable environment. While other researchers have made progress with one-off applications as well as custom content and mods for existing games, we are not aware of a general purpose application for empirically studying a broad set of user interactions in the FPS context.

The history of computer graphics is dominated by the quest for photorealism. Yet esports gamers have not benefited from the results, regularly turning off all visual effects to maximize frame rate. How can the SIGGRAPH community better support the expert performance esports athletes seek? This is not an idle question: esports now rivals traditional sports in both viewership and revenue, and is already driving demand for new, high-performance displays and mice.