Apple Releases Open-Source Model That Instantly Turns 2d Photos Into 3d Views
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The tech world is abuzz with Apple's latest open-source release, a model that instantly transforms 2D photos into 3D views, sparking curiosity and debate around the research team's diverse backgrounds. Some commenters pointed out that the authors' names suggest foreign origins, prompting a discussion on the role of foreign-born researchers in driving innovation in the US. While some noted that the US attracts top talent from around the world, others highlighted the difficulty in determining someone's birthplace based on their name alone. As the conversation unfolded, it became clear that Apple's global presence, with research labs worldwide, further complicates the narrative around national identity and scientific contribution.
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Paper: https://arxiv.org/abs/2512.10685
Just curious for those who are informed on this matter... are most research done by foreign born people? What happened to the big STEM push?
I don't mean to stir up political debate... just curious what the reality is, especially given the decline in foreign students coming over in recent year.
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...
The US is the largest research hub in the world, and it offers (or offered) outstanding conditions for research. I believe this to be as old as WW2, and it certainly didn't start with AI. Higher salaries, more diverse career opportunities (academia is more porous to industry in the US than many other countries), and the ability to hire more and better candidates for the workhorses of a lab: PhD students, postdocs, technicians, research scientists.
Re: supply side, undergraduate education (including Master's in some countries) has become basic infrastructure in a developed (or developing) country, and countries like China, ex-USSR or the western European nations have solid traditions in this regard, with many offering comparable (or surpassing) education to the best US universities in specific STEM topics. However, save for China, I believe a majority of these countries have not invested in research to match their growing pool of Master's (or even PhD) graduates.
Approximately 96% of the world's population is not American, so you should expect that really.
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.
This was universally acknowledged to be a great thing for the US until very very recently.
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.
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.
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?
(I guess there are other uses??)
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.
Each assumes you already have their developer environment configured to have the tool work, but simply don’t have it compiled yet.
SHARP, an approach to photorealistic view synthesis from a single image - https://news.ycombinator.com/item?id=46284658 - Dec 2025 (108 comments)
The “scenes” from that feature are especially good for use as lock screen backgrounds
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:
"Exclusively for research purposes" so not actually open source.
> The license must not restrict anyone from making use of the program in a specific field of endeavor. For example, it may not restrict the program from being used in a business, [..]
Open source means the source is available. Anything else is just political.
Where was that defined so? And most of all, given the domain of information technology, who understand open source to cover cases where the source is available ie. only for reviewing?
The purpose of words and terms is so that people can exchange ideas effectively and precisely, without needing to explain the terms every time from the grounds up. Having different groups having divergent definitions on the same words is counterproductive towards that goal. In my view, labeling a release "open source" with very big limitations on how the source is used is just not about marketing, it's miscommunication.
If "open source" and "source available" (and "open weights") mean the same thing, the how come people have come up with the two terms to begin with? The difference is recognized in official contexts as well, i.e. https://web.archive.org/web/20180724032116/https://dodcio.de... (search for "source available"; unfortunately linking directly doesn't seem to work with archive.org pages).
It doesn't seem there is any benefit in using less precise terms when better-defined ones are available.
Anything more than the definition is extra. In this case it's political. That doesn't require a definition.
> The purpose of words and terms is so that people can exchange ideas effectively and precisely, without needing to explain the terms every time from the grounds up.
Do you think the 10 criteria of a non-profit's opinion effectively conveys information without needing to explain the terms from the ground up?
> Having different groups having divergent definitions on the same words is counterproductive
Right, which is why the parent is wrong. It's just an organization's opinion
> If "open source" and "source available" (and "open weights") mean the same thing
They don't mean the same thing and I never claimed they do.
> It doesn't seem there is any benefit in using less precise terms when better-defined ones are available.
Use the more precise terms then. But you can't say the definition of open source is this 10 points of criteria that people disagree about....
We don't have to have this debate again. Folks have tried this rhetorical tack so often there is an entire wikipedia page[1] dedicated to explaining the difference between source available and open source...
[1]: https://en.wikipedia.org/wiki/Source-available_software
"Conversely, Richard Stallman argues the "obvious meaning" of term "open source" is that the source code is public/accessible for inspection, without necessarily any other rights granted"
See, here's another Wikipedia article with another opinion that disagrees, and RMS is obviously an authority.
https://en.wikipedia.org/wiki/Open_source#%22Open%22_versus_...
All of this is pointless so the common and accepted definition should be preferred. Which does not add extra political criteria for requirements.
In case you don't know, the dictionary doesn't prescribe meaning, it catalogs the commonly accepted usage of a word. That's why it listed "literally" to mean "figuratively", because along the way, people started using "literally" to mean "figuratively", so that's the definition now.
The common definition of open source does not match the OSI definition...
> people generally use “shared source” or something
"Shared source" was a Microsoft license and not at all generally accepted by anyone. A quick Google search found no references to this term used to describe open source.
Also if there are restrictions you can argue it doesn’t even meet that definition because it’s not “freely available”
The definitions from the FSF and OSI are very similar, and the lists of approved licenses are mostly the same.
AGPLv3 has provisions that are predicated on remote interaction over computer networks. Put modified AGPLv3 software on a computer that users interact with over RS232 terminals and you don't have to give users the source. Replace those RS232 terminals with X servers that let the users interact with the program over Ethernet and you do have to give those users the source.
You also need to explain how a set of terminals connected via RS232 does not constitute a "network".
Models can't be open source anyway, because they don't have source.
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.
Everyone will be able to flex their muscles as a creative. Everyone will be able to become an artist (expressing themselves though their unique lens) without putting points into a mechanical skill that is dimensionally orthogonal to idea expression and communication.
This is the "bicycle of the mind" that Steve Jobs talked about 40 some years ago. We've all had keyboards with which to express ourselves and communicate, but soon everyone will be able to visually articulate themselves and their thoughts. It's going to be so uplifting for society.
In fifty years we'll even be able to render our direct thoughts and mold them like clay. Share them directly with one another. Co-think.
Any of the code that wraps the model or makes it useful is subject to copyright. But the weights themselves are as unrestricted as it gets.
Ok, but there's clearly more nuance there. Otherwise I could claim that any mp3 file I wanted to distribute is just a table of 8-bit integers and therefore not subject to copyright.
I don’t know where the catch is, but that sentence can not be true in general.
Weights are numbers that come from an optimisation process. To the extent that weights encode any creativity, they encode the creativity of the training data. But any company using AI models (including Apple) does not want that interpretation because they are using AI models that were trained on other people's copyrighted works. If weights could be copyrighted, we all of us would own them.
I'm going to match this energy whenever I see it.
You either a deliberately misrepresenting the facts or been livoning under a rock. I mean read any discussion about M laptops and you see apple fanboys noncritucally declaring them a revolution in computing.
I see a lot of people extolling the battery life, displays, and trackpads. And probably an equal amount of complaining about the increasingly locked-down and un-customisable nature of macOS. We all like the hardware, and fight the software more by the day.
The blind zealots of the "I'm a Mac, and I'm a PC" era just aren't very common anymore.
https://news.ycombinator.com/newsguidelines.html
Yes, but the most important reason to pay attention to ANY license for most people is because it is a signal for under what conditions the licensor is likely to sue you (especially in the US, which does not have a general “loser pays” rule for lawsuits), not because of the actual legality, because a lawsuit is a cost most people don’t want to bear while it is ongoing or cover the unrecoverable costs of once it is done, irrespective of winning and losing, and, on the other hand, few people care about being technically legal with their use of copyright protected material if there is no perceived risk of enforcement.
But even if that wasn’t true, and being sued was of no financial or other costs until the case is finally resolved, and only then if you lose, I wouldn't bet much, in the US, in the court system ultimately applying precedent in the most obvious way instead of twisting things in a way which serves the interest of the particular powerful corporate interests involved here.
I know this is a long, nuanced, ongoing discussion. I'm very interested in it, but haven't read up on it for years. Could you elaborate a bit on the latest?
I was always in the camp that opined that "weights" are too broad a term for any sensible discussions about conclusions like "are (not) copyrightable". Clearly a weight that's the average of its training data is not copyrightable. But also, surely, weights that are capable of verbatim reproduction of non-trivial copyrightable training data are, because they're just a strange storage medium for the copyright data.
What am I missing?
Open source does not require full working implementations. There's no requirement that a code snippet that I release be fully working and identical to a complete solution.
About the access of binaries or providing working implementations, where did those come from? I don't think this thread was discussing those at all.
Indeed I would be willing to call something an "open source model" if it came without weights, but did come with the training data and with a documented process (preferably executable); and a release with just the training data could be called "open dataset" while the software to run the training would be just plain old open source software.
And, of course, a model with only the model data distributed with an open license is relatively commonly called "open weights", this being pretty self-explanatory term.
You already have access to all the training data everyone else is using.... You can download an offline version of Wikipedia. Here's every Reddit comment for a decade: https://academictorrents.com/details/ba051999301b109eab37d16...
Though, I do think it's still acceptable if you just point how to get the data (i.e. if it was the offline version of Wikipedia and then URL to that) if actually providing the source data is overwhelming. Offering to provide a copy at cost would be quite acceptable (i.e. I deliver the media to you to make a copy).
But if there's no way another person can acquire that data, even in theory, then I think it's pretty clear the source was not open. Just use the more appropriate term and everyone is on the level what the release is about.
The only reference seems to be in the acknowledgement, saying that this builds ontop of open source software
[1] https://github.com/apple/ml-sharp/blob/main/LICENSE
[2] https://fedoraproject.org/wiki/Licensing/Apple_MIT_License
It effectively prevents the community from using Apple's solution, but gives the Chinese everything they need to duplicate the results and push their own version.
I expect a Hunyuan-branded version of this model in six months. Probably with lots of improvements.
I'm all for Chinese model takeover if this is how US tech giants treat AI. You can't horde the flames forever, US hyperscalers.
The DoD ought to be advocating for a strong domestic open source stance to ensure our ecosystem doesn't get washed away. AI czar David Sacks has this view, but I suppose it's been falling on deaf ears when the hyperscalers crowd out the conversation.
https://github.com/apple/ml-sharp/blob/main/LICENSE
Between this and the model's license, it seems like one is stuck with using this for personal use?
People slowly waking up to how daft and hypecycle misusing a term was all along has been amazing.
https://www.downloadableisnotopensource.org/
Though I'm sure they will shut their shop asap now that Nvidia basically bought them.
I know, I'm joking: Trump likes Nvidia, but maybe he'll bump the Chinese tax to 30% to approve this deal? In a way I hope he pulls something like that, to punish Huang for his boot shining manipulations.
#iwantRAM
Open Source =/= free or software, just readable
so it wasn't a new campaign, it is at best re-appropriating the term open source in the software community in a way communities outside of software have always been using it, in a way that predates software at all, exists in parallel to the software community, and continues to exist now
I have also never once heard anyone use the term FOSS outside of the written form.
So the opposite of what you said, I guess.
You also seem to be saying that the term "open source" existed before software did, so I feel compelled to ask: what do you think "source" stands for in "open source"?
people need to re-evaluate their relationship with open source instead of as a synonym for FOSS, because it clearly doesn't mean that regardless of the colloquialism
and FOSS has an adjective and noun for a reason, its older than the colloquialism
this is just a reversion to the mean
New movements like "fair source", which is a form of source available + free use + expiring copyright is the ideal license. It's effectively unlimited use for customers and users, but has a baked in "non-compete" preventing low effort third parties from coming in and eating the market established by the authors.
We need to kill open source purity. Especially if we want to erode the hyperscalers that aren't even giving away the magic parts or valuable parts of their kingdoms.
Open source purity is a socialist dream while we're still living under the Empire. And it prevents the formation of a salient that can punch through.
I don't see any reason why you would want fair source authors to go "OSI" open other than taking their revenue stream as your own.
And by doing so you make the business unsustainable and impossible to scale into something formidable that could chip away at entirely closed hyperscalers.
They just ask you not to compete with them for a few years.
How is that any way comparable to AWS?
Perfect truly is the enemy of good.
No Open Source license actually permits this - by definition of Open Source.
Also the notion that copyright "expires" is ludicrous - we only just saw work from the 1920s enter the public domain (and source is no different to that). Laundering via AI clearly does not count, either.
this is a reversion to the mean
Linux kernel and GCC are probably the only thing left they tolerate, and even then, it is less relevant in the cloud, with containers powered by type 1 hypervisors.
> “Research Purposes” means non-commercial scientific research and academic development activities, such as experimentation, analysis, testing conducted by You with the sole intent to advance scientific knowledge and research. “Research Purposes” does not include any commercial exploitation, product development or use in any commercial product or service.
https://opensource.org/osd
If (which the courts seem to be pretty consistently finding) training models on copyright-protected works generally is fair use, though using models to produce works which would violate copyright if made by other means with reference to the source material is still a copyright violation, then training has no bearing on the legality of copying the models. (Even if it wasn't, then copying and usinf the models at all would violate the copyright of the original owners of the training material again and be illegal irrespective of the “license” offered by the model trainer.)
Morally? Well, pretty much the same dichotomy applies; if training the model isn't a violation of the source material's creators' rights, then the fact it was trained without permission has no bearing on the morality of using the model without the trainers permission, if it is a violation of the source material's creators' rights, then so is using the model irrespective of the trainer's “license”, as the trainer has no right to permit further use of the material they had no right to create.
https://github.com/apple/ml-sharp/blob/main/LICENSE
https://github.com/TencentARC/StereoCrafter https://github.com/TencentARC/GeometryCrafter
I’d be keen too.
doesn't seem very accurate, no idea of the result with a photo of large scene, that could be useful for level designers
I managed to one-shot it by mixing in the mesh exporter from https://github.com/Tencent-Hunyuan/HunyuanWorld-Mirror
Note that the results might not be as good as you expect, because this does not do any inpainting. So your mesh will be either full of holes or warped unless you layer on other techniques outside the scope of this model.
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