Thanks! Macroexpanded:

SHARP, an approach to photorealistic view synthesis from a single image - https://news.ycombinator.com/item?id=46284658 - Dec 2025 (108 comments)

I love how virtually no GitHub instructions related to AI simply work as written.

Each assumes you already have their developer environment configured to have the tool work, but simply don’t have it compiled yet.

imo https://x.com/SadlyItsBradley/status/2001227141300494550 is a better demo than their own project page

The readme doesn't claim its open source either from what I can tell. Seems to be just a misguided title by the person who submitted it to HN

The only reference seems to be in the acknowledgement, saying that this builds ontop of open source software

Meta’s campaign to corrupt the meaning of Open Source was unfortunately very successful and now most people associate releasing the weights with open source.

There's no reason to believe that weights are copyrightable. The only reason to pay attention to this "license" is because it's enforced by Apple, in that sense they can write whatever they want in it, "this model requires giving ownership of your first born son to Apple", etc. The content is irrelevant.

Interesting, the main license doesn't mention the limit, maybe that's why the op got confused

https://github.com/apple/ml-sharp/blob/main/LICENSE

I’m going to research if I can make a profitable product from it. I’ll publish the results of course.

Should the title be corrected to source-available?

That sucks.

I'm writing open desktop software that uses WorldLabs splats for consistent location filmmaking, and it's an awesome tool:

https://youtube.com/watch?v=iD999naQq9A

This next year is going to be about controlling a priori what your images and videos will look like before you generate them.

3D splats are going to be incredibly useful for film and graphics design. You can rotate the camera around and get predictable, consistent details.

We need more Gaussian models. I hope the Chinese AI companies start building them.

Is there any model that is actually free as in freedom (not necessarily gratis)?

Nice to see some more interesting use of this kind of educational source licensing

Your daily reminder that neural network weights aren't creative work and as such aren't subject to copyright protection in the first place. The “license” is purely cosmetic (or rather, it has an internal purpose: it's being put there by the ML scientists who want to share their work and have to deal with the corporate reluctance to do so).

When AI and open source is used together you can be sure it's not open source.

If all these AI models were trained on copyrighted materials for which the trainers had no right to, is it wrong to steal their models and use them however we want? Morally I'd say absolutely not, but I"m sure these AI bros would vigorously defend their own IP, even if it was built on stolen IP created by humans.

[flagged]

It’s open source, just not open domain.

Thanks! This looks nice

Unfortunately not as significant as you'd think.

The output is not automatically metrically scaled (though you can use postprocessing to fix this, it's not part of this model). And you can't really move around much without getting glitches, because it only inferences in one axis. It's also hard capped at 768 pixels + 2 layers.

Besides depth/splatting models have been around for quite a while before this. The main thing this model innovates on is inference speed, but VR porn isn't a use case that really benefits from faster image/video processing, especially since it's still not realtime.

This year has seen a lot of innovation in this space, but it's coming from other image editing and video models.

Gives the term "Gaussian splat" an entirely different meaning...

I'm glad you said it. Incredible tech and the top comment is debating licensing. The demos I've seen of this are incredible and it'll be great taking old photos (that weren't shot with a 'spatial' camera) and experiencing them in VR. I think it sums up the Apple approach to this stuff (actually impacting peoples lives in a positive way) vs the typically techie attitude.

> which isn't unsurprising

There has to be an easier combination of words for conveying the same thing.

I don't think it isn't unsurprising :)

Wait so you are surprised?

I would love to see your examples.

It seems like it, although the shipped feature doesn’t allow for as much freedom of movement as the demos linked here (which makes sense as a product decision because I assume the farther you stretch it the more likely it is to do something that breaks the illusion)

The “scenes” from that feature are especially good for use as lock screen backgrounds

I assume this is the same spatial scenes feature that was on visionOS prior to OS 26. In my experience that was really incredible. You could take a standard 2D photo of someone and suddenly you were back in the room with them.

Ya, I like when it’s automatically done on my featured photo, gives the phone a very 3D look and feel.

I am thinking the same thing, and I do love the effect in iOS26

Also, frames from live photos

You could use pixi instead, as a much nicer/saner alternative to conda: https://pixi.sh

Though in this particular case, you don't even need conda. You just need python 3.13 and a virtual environment. If you have uv installed, then it's even easier:

    git clone https://github.com/apple/ml-sharp.git
    cd ml-sharp
    uv sync
    uv run sharp

You can simply use a `uv` env instead?

You aren't being irrational.

I hate pip, a million times worse than conda

Weird how “hugging face” is a heartwarming little smiley face, while “face hugger” is a terrifying alien xenomorph. Seems like there’s an analogy to be made there…

no

It doesn't but it's pretty trivial to do if all you want is a pinholed mesh.

I managed to one-shot it by mixing in the mesh exporter from https://github.com/Tencent-Hunyuan/HunyuanWorld-Mirror but at that point you might as well use HWM, which is slower but much better suited to the level design use case.

Note that the results might not be as good as you expect, because this does not do any angled inpainting -- any deviation from the camera origin and your mesh will be either full of holes or warped (depending on how you handle triangle rejection) unless you layer on other techniques far outside the scope of this model.

And note that although HWM itself does support things like multi-image merging (which ml-sharp does not), in my testing it makes so many mistakes as to be close to useless today.

If you want something different that is designed for levels, check out Marble by World Labs.

No

Gaussian splats

Curious what this has to do with the post?

Apple has not been nice and open since the 1970s. The only open and nice person in any important role is Wozniak.

Apple absolute Never believed in open source in the past so yes. They are not the same

If you're concerned by that, I have some bad news about instant noodles.

What would your definition of "instantly" be? I would argue that, compared to taking minutes or hours, taking less than a second is fast enough to be considered "instant" in the colloquial definition. I'll concede that it's not "instant" in the literal definition, but nothing is (because of the principle of locality).

For panning you don't need a 3D view/reconstruction. This also allows translational camera movements, but only for nearby views. Maybe I am overly pedantic here, but for HN I guess thats appropriate :D

It will be used for spatial content, for viewing in Apple Vision Pro headset.

In fact you can already turn any photo into spatial content. I’m not sure if it’s using this algorithm or something else.

It’s nice to view holiday photos with spatial view … it feels like you’re there again. Same with looking at photos of deceased friends and family.

It's included in the ios photo gallery. I think this is a separate release of the tech underneath.

This is a free research project on GitHub. I think I'd rather apple focus on making hardware than hoarding GPUs for PR stunts to prove they are a "serious company".

We detached this subthread from https://news.ycombinator.com/item?id=46402044 and marked it off topic.

I'm not trying to be too pc, but you can't really tell based on someone's name where they were born.

That said, the US only has some 5% of the worlds population (albeit probably a larger proportion of the literate population), so you'd only expect some fraction of the world's researchers to be US born. Not to mention that US born is an even smaller fraction of births (2.5-3%, by Google), so you'd expect an even smaller fraction of US born researchers. So even if we assume that we're on par with peer countries, you'd only expect US born researchers to be a fraction of the overall research population. We'd have to be vastly better at educating people to do otherwise, which is a longshot.

Obviously this makes turning away international students incredibly stupid, but what are we to do against stupidity?

are most research done by foreign born people

Approximately 96% of the world's population is not American, so you should expect that really.

1. People with foreign sounding names may have been born in the United States.

2. People who were born outside the United States but moved here to do research a while back don’t suddenly stop doing research here.

FWIW, many of the researchers on the paper did not study in the U.S. but immigrated after their PhD studies.

I checked the first, middle, and last author: Lars Mescheder got his PhD in Germany, Bruno Lecouat got his PhD in France, Vladlen Koltun got his PhD in Israel.

(Edit: or maybe they did not actually immigrate but work remote and/or in Europe)

Why don't we produce enough experts in the US to saturate our tech companies?

It's because American education culture is trash. American parents are fine with their kids getting Bs and Cs. Mediocrity is rewarded and excellence is discouraged in our schools, both socially and institutionally.

Meanwhile you have hundreds of millions of foreign born children pulling out all the stops to do the best they possibly can at school precisely so they can get into the US and work at one of our top companies.

It was never even a competition. Immigrants and children of theirs will continue to outperform because it is literally baked into their culture - and it is baked out of ours.

Apple is also a global company and has offices and research labs world wide. At least a couple of the authors seem to work for Apple but at their German lab.

How do you know where the authors were born?

foreign to... where?

It makes sense you're getting downvoted but I thought it was actually an interesting question so I spent the past hour or so doing an autistic rabbit hole (including finding the linkedins of the folks on the paper linked here to understand their backgrounds), heh.

Was somewhat surprised to learn that the pipeline wasn't built by industry demand, it was supply pressure from abroad that happened to arrive just as US universities needed the money (2009/10). In 1999, China's government massively expanded higher education, combined with a system where the state steers talent into stem via central quotas in the "gaokao", it created an overflow of CS capable graduates with nowhere to go domestically, India's 1991 liberalization created the IT services boom (TCS, Infosys, Y2K gold rush) and made engineering THE middle class ticket, so same overflow problem. US phd programs became the outlet for both countries.

In that light, the university side response probably wasn't state side industry demand for loads of PhDs, who was hiring those then? Google Brain didn't exist until 2011, FAIR until 2013. It wasn't really till 2012+ that industry in tech started to hire big research groups to actually advance the field vs specialized PhDs here and there for products... so not a huge amount of pull from there. Then, at the same time, universities were responding to a funding crisis... there was a 2008 state budget collapse, so it was backfilled with international Master's students paying $50-80k cash (we do this in Canada heavily also), that revenue cross-subsidized PhD programs (which are mostly cost centers remember). I also read some say PhD students were also better labor: visa constraints meant they couldn't easily bounce to industry, they'd accept $30k stipends, tho I saw other research contradicting this idea. The whole system was in place before "AI Researcher" was even a real hiring category. Then deep learning hit (2012), industry woke up, and they found a pre built pipeline to harvest: The authors on that Apple paper finished their PhDs around 2012-2020, meaning they entered programs 2009-2015 when CS PhDs were already 55-60% foreign born. Those students stayed, 75-85% of Chinese and Indian STEM PhDs are still here a decade later. They're now the senior researchers publishing papers you read here on HN.

This got me wondering, could the US have grown this domestically? In 2024 they produced ~3,000 CS PhDs, only ~1,100 domestic. To get 3,000 domestic you'd need 2.7x the pipeline...which traces back to needing 10.8 million 9th graders in 2018 instead of 4 million (lol), or convincing 3x more CS undergrads to take $35k stipends instead of $150k industry jobs. Neither happened. So other countries pay for K-12 and undergrad, capture the talent at PhD entry, keep 75%+ permanently.

Seems like a reasonable system emerged from a bunch of difficult constraints?

(and just to reiterate, even tho it was an interesting research project for me, you can't infer where someone is directly from based on their name)

https://sccei.fsi.stanford.edu/china-briefs/highest-exam-how...

https://en.wikipedia.org/wiki/Economic_liberalisation_in_Ind...

https://ncses.nsf.gov/pubs/nsf24300/data-tables

https://www.aau.edu/newsroom/leading-research-universities-r...

https://ncses.nsf.gov/pubs/nsf25325

https://www.science.org/content/article/flood-chinese-gradua...

https://www.insidehighered.com/quicktakes/2017/10/11/foreign...