The method currently runs at what the researchers-Stephan R. The researchers compared their method to other approaches, many of which struggled with those two points in particular. This approach is what gives the method its great temporal stability (moving objects look geometrically consistent from one frame to the next) and semantic consistency (objects in the newly generated frame correctly represent what was in the original frame). Rather than looking only at the final frame rendered by the game engine, the method from theВ Intelligent Systems Lab looks at all of the extra data available in the G-buffer to make better guesses about which parts of its photorealistic dataset it should draw from in order to create an accurate representation of the scene. An example of G-buffer data | Image courtesy Intel ISLДЄ G-buffer is a representation of each game frame which includes information like depth, albedo, normal maps, and object segmentation, all of which is used in the game engineвЂ™s normal rendering process. This вЂstyle transferвЂ™ approach isnвЂ™t entirely new, but what is new with this approach is the integration of G-buffer data-created by the game engine-as part of the image synthesis process. An example of a frame from GTA V after being enhanced by the method | Image courtesy Intel ISL The method creates an entirely new frame by extracting features from the dataset which best match whatвЂ™s shown in the frame originally generated by the GTA V game engine.

But while DLSS is designed toВ ingest an image and then generate a sharper version of the same image, the method from the Intelligent Systems Lab ingests an image and then enhances its photorealism by drawing from a dataset of real life imagery-specifically a dataset calledВ CityscapesВ which features street view imagery from the perspective of a car. In concept, the method is similar to NVIDIAвЂ™s Deep Learning Super Sampling (DLSS). The results are quite impressive, showing stability that far exceeds similar methods. Researchers from IntelвЂ™s Intelligent Systems Lab have published research demonstrating a state of the art approach to creating truly photorealistic real-time graphics by layering a deep-learning system on top of GTA VвЂ™s existing rendering engine. While real-time raytracing takes us another step toward realistic graphics, thereвЂ™s still a gap between even the best looking games today and true photorealism. Even so, itвЂ™s far from what would truly fit the definition of вЂњphotorealistic.вЂќДЄlthough weвЂ™ve been able to create pre-rendered truly photorealistic imagery for quite some time now, doing so in real-time is still a major challenge. While the technique in its research state is too slow for real gameplay today, it could represent a fundamentally new direction for real-time computer graphics of the future.Дespite being released back in 2013, GTA V remains a pretty darn good looking game. Demonstrated with GTA V, the approach uses deep-learning to analyze frames generated by the game and then generate new frames from a dataset of real images. Researchers fromВ IntelвЂ™s Intelligent Systems Lab have revealed a new method for enhancing computer-generated imagery with photorealistic graphics.

Gta real life v