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NVIDIA Intros DLSS 2.0: Ditches Per-Game Training, Adds Motion Vectors for Better Quality

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While NVIDIA’s annual GPU Technology Conference has been extensively dialed back and the bulk of NVIDIA’s announcements tabled for another day, as it turns out, the company still has an announcement up their sleeve this week. And a gaming-related announcement, no less. This morning NVIDIA is finally taking the wraps off of their DLSS 2.0 technology, which the company is shipping as a major update to their earlier AI-upscaling tech. Responding to both competitive pressure and the realization of their own technology limitations, the latest iteration of NVIDIA’s upscaling technology is a rather significant overhaul of the technique. While NVIDIA is still doing AI upscaling at a basic level, DLSS 2.0 is no longer a pure upscaler; NVIDIA is now essentially combining it with temporal anti-aliasing. The results, NVIDIA is promising, is both better image quality than DLSS 1.0, as well as faster integration within individual games by doing away with per-game training. As a quick refresher, Deep Learning Super Sampling (DLSS) was originally released around the launch of the Turing (GeForce RTX 20 series) generation in the fall of 2018. DLSS was NVIDIA’s first major effort to use their rapidly growing experience in AI programming and AI hardware to apply the technology to image quality in video games. With all of their GeForce RTX cards shipping with tensor cores, what better way to put them to use than to use them to improve image quality in games in a semi-abstracted manner? It was perhaps a bit of a case of a hammer in search of a nail, but the fundamental idea was reasonable, especially as 4K monitors get cheaper and GeForce 2080 Tis do not. Unfortunately, DLSS 1.0 never quite lived up to its promise. NVIDIA took a very image-centric approach to the process, relying on an extensive training program that involved creating a different neural network for each game at each resolution, training the networks on what a game should look like by feeding them ultra-high resolution, 64x anti-aliased images. In theory, the resulting networks should have been able to recognize how a more detailed world should work, and produce cleaner, sharper images accordingly. Sometimes this worked well. More often the results were mixed. NVIDIA primarily pitched the technology as a way to reduce the rendering costs of higher resolutions – that is, rendering a game at a lower resolution and then upscaling – with a goal of matching a game’s native resolution with temporal anti-aliasing. The end results would sometimes meet or beat this goal, and at other times an image would still be soft and lacking detail, revealing its lower-resolution origins. And all the while it took a lot of work to add DLSS to a game: every game and every resolution supported required training yet another neural network. Meanwhile, a simple upscale + sharpening filter could deliver a not-insignificant increase in perceived image quality with only a fraction of the work and GPU usage. Enter DLSS 2.0 While DLSS 1.0 was pure, in retrospect it
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