(github.com)
> The inference interface uses the engine-rendered RGB image as a dense, registered observation of visible scene appearance. It provides dense, pixel-aligned evidence for object support, occlusion boundaries, composition, and local material properties; engine motion vectors separately provide temporal correspondence.
> Existing image generative models commonly rely on text embeddings, exemplar images, or spatial control fields such as depth, edges, segmentation, and pose [...] These conditions are effective for general-purpose generation and editing, but they do not uniquely determine the object identities, materials, visibility relationships, lighting decisions, and pixel-aligned detail contained in an engine-rendered frame. DLSS 5 is therefore conditioned on the rendered frame itself.
I'd also be interested in how post-processing fits in with this. Like if you've got weather effects, film grain, tone mapping, etc, I would have thought the model would do better working on the image before those processes.
> I'd also be interested in how post-processing fits in with this.
I think screenspace effects like film grain and tonemapping are excluded in the same way UI elements are rendered separately from the game.
WoW goes to lengths to hide its depth buffer.
What kind of sorcery is this ? Very impressive work !
With LLMs you can do whatever you want pretty much. I have upstream CUDA running llama.cpp under unmodified Nouveau on one of my boxes. Why? Well, why not?
I also have a modified Nouveau driver that, with the help of more and newer blobs, gets reclocking working for at least most of Pascal/GTX 10 series. I would love to try to upstream it but it desperately needs to be rewritten with that intent. Too much ugly garbage. Still, I wanted to know how possible it is. Possible, it turns out. Modern LLMs can blackbox analyze the real driver quite well, and debug the Falcons themselves. It's very interesting. People say coding is dead; I think it's probably not really true. However, it is certainly changing. I think someone less skilled than me could beat me to the punch with enough determination. That is interesting.
Nowadays it takes one well written prompt to a frontier LLM to produce something like this.
LLMs are really good at deobfuscating or even decompiling code.
Programmer: OpenDLSS...
Jensen : Wait. Not like that! (╯°□°）╯︵┻━┻
Even in a "neural rendering optimistic" future, I can't see a way in which traditional rendering doesn't survive as a "control channel" that informs what the neural rendering does. To do otherwise would require making the bulk of the game logic neural too.
ofc, getting performance is the real problem. I like neural rendering ideas in the sense DLSS can make certain things happen on older machines that were definitely out of scope for deferred pipeline on same machine.
Its like a Boost u really dont want to use if u dont need it.
It might become that CGI will he the place for very accurate rendering more than games, but i sure hope not. (they can easily afford accuracy as they dont need realtime).
games need to slow down a notch on getting better graphics and go for a stabilization and expectation management pass. so people dont expect things that are only feasible through neural rendering techniques. It forces a lot of players to use it. like most AI. fomo or something...
Neural-assisted rendering tech is awesome because it actually exploits the spatial/temporal redundancy of the image stream to save render compute. There are very few techniques that can do it, and none I know can match the neural quality.
Frame 2 is highly redundant if you already have Frame 1 rendered, and even more so if you can supply the motion vectors. If you already have Frame 3 too? It becomes extremely redundant.
And yet, a conventional rendering pipeline would spend as much computation on it as it would on Frame 1. There is no "just reuse the previous work for cheap" primitive in conventional rendering. Every frame has to be built step after step, with the full depth of the rendering pipeline.
The same is true for neural upscaling. Going from 1080p to 4K means pushing 4x the pixels - and for a conventional renderer, that's nearly 4x the work. But a neural upscaling pipeline can exploit the redundancy of image data - and "fill in" the missing detail from a "ground truth" 1080p render, in a way that flies below the radar of human perception. Using cheaper neural operators instead of the full pipeline for it.
Neural rendering is a welcome optimization to conventional rendering techniques, in my eyes. As long as it's implemented right. Which is hard, but not impossible - we've come a long way already.
Also, I think "hard generative AI" is more useful in CGI than in real time rendering. Because real time rendering with user input demands a degree of repeatability, but CGI only has to "look good once". So you can accept the lowered accuracy of a largely untethered generative process with very few keyframes, and pick the "better" (more accurate, if that's what you want) outputs out of it.
This is great until the redundancy no longer exists and you have to rebuild the entire state machine from zero (a humble scene transition or rapidly turning a corner).
Gbuffers have been using motion vectors forever. For realtime global illumination, techniques like ReSTIR already allow for temporal reuse and spatial coherence.
I just don't see the purpose of replacing the traditional rendering pipeline for neural rendering techniques. It would be one thing if we were constrained on the number of triangles we could push per second, but we're not. The bottleneck is usually elsewhere in the pipeline: animating characters/objects, particle systems, physics updates, etc.
But the line is drawn when it involves CUDA and any part of their closed source compilers (nvcc).
There are obvious reasons why they are closed source, but it’s becoming pointless since Deepseek have open sourced their AI compiler and compute libraries with DeepGEMM and eventually they will catch up.
At least their support helps the open-weight ecosystem.
They care when the agendas align, and they don't when they won't.
The current implementation is more of a tech demo than a practical way to play games (+ officially it's available in 1 game). It's _fast enough_ to make some impressive YouTube videos but you most likely won't want to play anything with it yet.
Nvidia has stated that they're still working on improving the performance. No doubt future hardware generations will also include further hardware optimisations.
The potential for this kind of technology is pretty awesome, especially given that people have also found ways to add this to emulators.
Again, I hope I’m wrong and we see new cards summer/autumn 2027, but I would not bet my savings on it.
Corporations do not have a fiduciary duty to seek maximal profits. This is a myth.
They are given wide latitude to decide what's in the best interests of the shareholders. Keeping a less-profitable offering alive just in case the current big offering doesn't pan out in the long term would easily be defensible in court.
It wouldn't even be a challenge. Courts are loathe to question the judgment of directors and executives. The reasoning is obvious: why in the world would a judge have better knowledge of how to run a company than the people whose jobs are to run the company?
In some cases, it seems that lowering resolution and graphics actually produces better DLSS5 output (but it varies)
It's really good at making older gen games look remade/remastered.
RTX 5060: 9.9 ms at 1080p
RTX 5070: 10.2 ms at 1440p
RTX 5080: 13.7 ms at 2160p
RTX 5090: 8.2 ms at 2160p
Isn't the mote that Nvidia has is they work with studios to generate the training data from the game, then they ship a model per game?
Or is my knowledge outdated here and they're just using a single generalised model?
> Isn't the mote that Nvidia has is they work with studios to generate the training data from the game, then they ship a model per game?
That was true for the very first version of DLSS, from DLSS 2 on the models have been universal - the per-game adjustments are done on the inference end by changing the effect intensity or masking out objects
They have a technical report on the neural rendering part of DLSS 5 which goes into it: https://research.nvidia.com/labs/adlr/files/DLSS5_Report.pdf
They don't. Only DLSS 1 was trained specifically per each game.
there's no way that's true!?
Bit-identical, I swear I heard that somewhere before.
I think its somehow needs to talk (write) about the things that are in the context and removal is there so AI predicts that it should be there.
Especially egregious if both adding and removing the thing happens in one commit. Git should be telling the story, and if it can't then there _is_ no story!
AI probably should not be writing docs, commit logs or comments.
I do feel like there's merit to having an open source implementation of anything, no matter who/what wrote it. I'm just hoping the results are validated well.
bold of you to assume the code wasn't llm generated as well.
Unfortunately it is slop, beyond the comprehension of the author unless they are experienced with DLSS internals to explain it in depth.
This is how I feel about every single project announcement on HN recently, they are already bragging about models all over the place, why shouldn't they go full way down being replaced by the Borg?
> The temporal path is implemented, but in the demo: the network's history input lanes and its per-pixel blend logit drive a reprojected feedback loop (docs/frame.md). The dlss5vk tool runs single frames with no history, which is what the reference captures were made with.
From this I assume the network uses the (via motion vectors) reprojected previous frame in order to increase temporal stability, i.e. similarity over adjacent frames. But this isn't strictly necessary, and apart from it, DLSS 5 is a pure post-process filter. So you could apply it to an old animated CGI movie like Final Fantasy (2001) [1]. Which should make it look significantly more realistic, at the cost of some flicker or other temporal instability.
One could also apply it to still images, like old renders from Tomb Raider [2], where temporal stability is not a factor. The difference to conventional text-to-image models with a "make it photorealistic" prompt would be that DLSS 5 strongly adheres to the underlying geometry.
1: https://www.imdb.com/title/tt0173840/
2: https://www.tombraiderchronicles.com/images/artwork-high-res...
That doesn't guarantee maanHimself is the original author, but it's looking likely.
Copyright (c) 2026 maan
So... Either alooshdenny stole the commits, or it's an alias for maan.
It might be possible to pursue this as illegal under general copyright law but it would be difficult.
If you don't want something like this to happen to your work, use a stronger license.
Which is not to say that uploading something to github is necessarily asserting ones authorship (if you read the front matter of a book, you might see a phrasing such as "[Name] asserts the moral right to be identified at the author of this work."; this is intended for jurisdictions that comply with the Berne convention more fully than the US does.) It is also not an instrument of conveyance (which needs to be signed by the owner of the rights) or the same as registering a copyright. A copyright notice is no longer required since the Berne Convention Implementation Act of 1988 (effective as of 1989), but would involve the copyright symbol and not a git upload.
And in any case, the copyright notice is still intact. They did attribute the author.
Authorship and the underlying code are seperate rights under law.
Waive. And no, it doesn't but it doesn't require attribution either. So you're falling back on copyright law which is unlikely to protect you here - there's a reason people include licenses, after all.
just unsure who's original ))