A reminder that a state of the art GPU renderer can produce photorealistic scenes at 4k resolution (about 8000000 pixels) at 120 fps (i.e. render the scene in less than 8ms).
In other words, this performance is completely inaccessible to current DNN architectures (and I suspect will never be as efficient). As another algorithmic illustration, we have already near-optimal algorithms for sorting large lists of random numbers. I guarantee a large neural network even trained for massive lengths of time cannot beat the sheer efficiency of traditional algorithms. It should approach the asymptotics of the optimal algorithm, but there's too much overhead in how DNNs are structured. A better task, I believe, is to use DNNs to write code that improves renderers, shaders, or sorting algorithms while maintaining image quality (in the case of computer graphics) or correctness (in the case of sorting algorithms).
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In this case the problem could be coming up with a variety of good realistic textures. I'm sure Google could hire a few artists to do a fantastic job, but if you want overkill I guess a GAN could generate the textures (to be rendered by a conventional algorithm).