How Intel enhanced photorealism using machine learning techniques

These techniques meant a great step forward to the realism of computer graphics, but if you have a look at the most sophisticated real-time games, it will be quickly revealed that photorealism has not been achieved. There is still a remarkable difference between simulation and reality. Mostly because a physically accurate simulation requires computation power and time not compatible with the real-time response that the games need.

More recently, we have seen the growth of new complementary techniques in machine learning and computer vision. Techniques that are “based on deep learning, convolutional networks, and adversarial training, bypass physical modeling of geometric layout, material appearance, and light transport. Instead, images are synthesized by convolutional networks trained on large datasets.

Computer graphics haven’t stopped evolving, but even the most graphically stunning video games don’t look yet like realistic, real-world footage (the ultimate goal most of the time). This was the seed for Intel Lab researchers to develop a method to make synthetic images more realistic using neural networks and the results when applied to GTA V were impressive!

Unfortunately, you can’t actually play the game in this photorealistic mode since it’s only re-rendering the recorded footage…

Approaches like this interesting research may be cool to upgrade the graphics of older games, but turning towards advanced perception, would you train your neural network with similar methods?

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