The *unofficial* Two Minute Papers discord server is now available. If you wish to volunteer/help, please let the mod(s) know! Thank you so much! reddit.com/r/twominutepapers/comments/f9u640/discord_or_slack/
Use AI to generate random stories Use AI to generate photos from stories Use AI to generate videos from photos Use AI to upscale and make it 60fps An AI movie?
There's already a movie whose screenplay was generated by AI, then fully acted out. But that was a few years back, prior to transformers. I wonder how things have progressed since then.
First time I heard you say "Doctor". It rolled off your tongue smoothly! Beautiful! Congrats on your achievement! You have always been a PhD to us all.
Disclaimer, I don't know much about any of this past a surface level. But I'd imagine it might be because of the input reference materials used to train or design the AI having more generic lights. The lights for the 3D render of the Mini Cooper look like it's from an old Volkswagen Golf, which would be an ideal candidate for hatchbacks because of it's lack of a distinct feature; it's a generic car. I'd imagine that's what happens to the birds too, the AI just pulls from the reference images used to train it, and applies it accordingly.
@@CGPacifica You're right, it can pull from the input itself since it can see it clearly, why pull from the resources? I'm just thinking out loud but perhaps it recognized the input as a "vehicle" and knowing it's a vehicle means it has to be at least somewhat symmetrical. Maybe the lights, or just the features on the far side (the part it can't see) are too much for it to infer and so it just approximates it to reference material instead?
It looks like it's not so much converting a 2d image to 3d, but instead creating a 3d object from what it was trained on and using the 2d image as a source for basic features-- almost like if someone was describing the photo and someone else was creating a 3d object based on that description.
This low level of detail already has applications such as for making game prototypes, with more fidelity than whiteboxing, just step outside or walk a trail and capture stand in objects. OR and this is the big one just feed mockup images to it.
This explains visual perception to a high degree. It shows how people deconstruct the scene they're present in into objects, and it also shows why people can project their awareness into space and look at things from another perspective. It also explains out of body experiences.
the only thing I don´t understand: why does the AI image look so different in the same perspective as the input photo? Couldn´t they project the texture from the camera perspective onto the model, so at least the visible part of the model looks better? 3:35
They could and it'd be trivial, but it's not what they're trying to do. For a commercial application, google earth for example, it'd be an obvious improvement, but I believe here they're trying to generate everything from scratch to create more generalised and comprehensive method. This method will eventually work just from word descriptors for example, while projection mapped approach wouldn't.
If we did that it would, essentially, be cheating. The point is to see what the neural network is "seeing" and how it builds the image. They will of course continue to make the image better until it can one day create a perfect photo realistic recreation.
What they ought to do is have AI 2 test the finished output of AI 1 against that original angle and either reward or punish AI 1 for better and worse versions of the same object. This would train it on specific objects after which you'd have to train it on categories of things (e.g., 👎🐦 ➡ 👍🐦, 👎🦉➡ 👍🦉, 👎🦚 ➡ 👍🦚, 👎🦆 ➡ 👍🦆 ⏩ good bird)
Cool work! A few thoughts: A system like this is theoretically impossible if I want to use it for 'any ' object because a single input image does not contain enough information for the computer to know what the other sides look like or what 'thickness' anything has. The only reason why it does know here, is because it was trained on specific examples. If I were to get a random foreign object that was not in the training set, it would likely not be able to figure some things out. Second, I doubt the resulting model is at all a starting point for a 3D artist. They can't just 'add in the details' like you say, because the mesh itself likely has poor edge loops and is 'unhandleable' or unoptimized for any sort of software to be able to work with it well. Same goes for the generated UV and texture. Nothing beats the modeling and unwrapping skills of a human, because they truly understand what makes the most sense to do. That car UV looked really inefficient for example, using 4 different spots for every wheel while that could have been one. Still, it provides an interesting starting point and may venture into better applications in the future.
@@Crayphor I remember hearing how the developers behind Microsoft Flight Simulator 2020 used AI to help build the cities in that game from real maps. Something like this would probably have an even better effect. Perhaps, one day, we can have street view without the need for cars to 360-degree camera drives about the world.
I think Google Maps is using something based on the Category-specific Mesh Reconstruction method, or at least related to it. You usually see those jagged artifacts in Maps which appear in CMR.
I think what I love so much about these videos is that you can see that the technique is still not perfect, and that in the future we will likely look back on this and scoff, but still see it as one of many stepping stones in computer generation. You get the sense you’re watching history in the making.
You can still improve this neural network if it distinguishes between objects and represents their approximate geometry in space. In addition, she can determine where the wheel or any other part should be located, then color the delati and assemble the whole machine separately, if the low-poly modeling from one object, then determine at what point the glass is, etc. as a result of the photograph, she will be able to collect all that she knows from the details inside the space. Cars, animals, people
The hummingbird example -clearly this is a bird, so object detection / categorisation would tell the net that it needed two wings as its a bird! Humans can fill in the details becuase they would know that a bird has two wings and it is in flight so any different angle is going to show the birds wings... 2 more papers...
This technique has been around for over a decade! I saw a documentary called CSI NY back in 2009 where an investigator zoomed, rotated and enhanced a photo using a computer program like this.
If you added a classification/recognition + modification neural network, to this one, it should get pretty close to perfect. Whatever features the AI is unsure of, it can just assume that its like other birds that it knows about, like humans do. When human beings turn 2D into 3D, we fill in whatever we dont know with what we expect. I.e. when were looking at that bird, wed automatically fill in specific details about feather, head shape etc, that we normally see in birds.
This would serve as a huge bonus for not only robots but anything relating to entertainment which would be especially modeling for video games and movies. Really awesome to see.
The first thing I thought of when I watched this was that this algorithm could be part of a network that watches old movies, creates models of all the objects in a scene and then renders an enhanced version of the same footage. We 've already seen RNNs that can ensure temporal coherence and physics based simulations that would provide enough information to perhaps even enhance footage considered to be damaged beyond repair. Is it very far fetched to think that a similar approach might be able to do the same with audio? I 'm absolutely fascinated and can 't wait to see someone try that.
If I was a police officer looking at those 3d car models the blurry CMR looked more accurate to shapes and branding styles of the car manufacturer than the newer one. It was hard to tell what brand car the newer one was making.
That's amazing!!! Creating PS1 level graphics from a single photograph?!? That's insane! How would the results look if we took a synthesized image from previous works and fed it into this one? Would it end up with several artifacts? And if so, could we train a network to search for artifacts and further refine the generation process of Both of the generating networks?
This is one of those things that is incredibly technically difficult but can't always be appreciated. People who aren't aware of what goes into those renders might look at them and think "wow, that's incredibly fuzzy and inaccurate. It barely even looks like a _____!" But I'm very glad to be able to appreciate this technology with the others watching :)
This system is a huge step for automation, so autonomous driving systems can generate occluded or far sides of objects the way our minds do automatically!
I imagine this could be improved by an algorithm that mirrors the objects, since most natural and man-made objects are symmetrical. If the network could somehow find the mirror of least resistance (most overlap with mirror object) in the least destructive way, then small faults like single wings could be filtered out.
Yeah, I'm definitely thinking this will enhance photogrammetry for the time being, rather than outright replacing it. It'll probably make scanning reflective objects and parsing out shadows a lot easier, so objects wouldn't have to ideally be shot in diffuse ambient light to extract textures and minimize redlections.
it would make sense, of course there would be a lot of complexity to overcome. Conceptually you should be able to use trigonometry on 3 separate images to make a really accurate model, especially if the program knew the exact angle the picture was taken from.
As somebody who has reimplemented this paper for a dissertation, it does (usually) already use multiple views during training for better 3D comprehension. This is not mandatory, but tends to help a lot with the 3D shape
I bet that in 10 years from now we will be able to watch all our favorite movies in complete 3D reconstruction in real time, having the original movie as reference only. In 20 years from now, we will enjoy them as hologram projection movies.
Neural Rendering Engines are coming soon I imagine. A few years down the line and making a 3D scene might be as simple as describing it in plain text, with maybe a few example images.
Maybe giving the network the 3D axis and light direction can help a lot, or even make a miror of one face of the object to replicate so the final model is actually simetrical. I feel like a few features can greatly improve the end result
This is such a nice and curious community you gathered here! Congratulation on creating something socially significant in the digital age with digital measures :)
HUGE GAME CHANGER! WOW!!!!!!!!! never in my wildest dreams did I think we would get to this point. I knew we would though, however, because I do believe the human brain is a deterministic machine. Therefore anyting the human brain can do, can be done in computer hardware. I had no idea we were going to be this far along at this early of a stage!!! in a very real sense, the main takeaway from this particular neural network capability isn't so much the 3D extraction, amazing though that is, as much as it is a proof of the deterministic nature of our own neural networks. and I am taking great liberty with the term neural networks as it applies to the human brain. The simulation of neural networks within computers is only an approximation of the deterministic machine we have between our ears. I would imagine that this particular neural network has really raised some eyebrows in the philosophical community and I believe it could be used as a very strong argument for the deterministic nature of the brain. There are some philosophers that believe we are non-deterministic, and that there is some metaphysical operations at work. Shear and pure fantasy and wishful thinking! I remember there was a time when philosophers pointed to chess as being unsolvable by computers as a proof for the non-deterministic nature of the human brain. no matter how far they move the goalposts, the computers keep catching up. It's like the gaps logic in religion, it continuously gets eroded by technology. This is a fine example of that erosion.
This teases the possibility of finally having a useful robotic house cleaner. Especially with iterative feedback from the actual environment into the planner.
what I'm getting out of this is that the pixel 7's big stand out features going to be making 3d interactive worlds out of single images. Which you can send to your friends to play with on whatever google rebrands Stadia into. VR is going to get so dope.
From now on, if I ever hear someone saying "hey dude did you see the last Two Minute Papers video, you know, with Károly Zsolnai-Fehér?" I will shout, "DOCTOR GODDAMMIT DOCTOR!"
I wonder if you could use a library of existing 3d models, then use image analysis to work out what is in the image, and find a relavent 3d model to use as a starting point
The *unofficial* Two Minute Papers discord server is now available. If you wish to volunteer/help, please let the mod(s) know! Thank you so much! reddit.com/r/twominutepapers/comments/f9u640/discord_or_slack/
I'm in
is good
Two Minute Papers when did you become a doctor?
Why is it unoficcial?
@@FeatherSlowfall cause official two minute papers doesn't own it
last time I was this early, Dr Károly Zsolnai-Fehér wasn't a doctor yet
Lol me 2, I'm glad he's now. He deserves it.
Same here. I'm so glad that he's called Doctor now.
You missed a video then
He was born a doctor
I could never in hundred years know how to write his name
I was so surprised when you said “doctor” that I dropped my papers. I wish someone warned me to hold on to them. Congratulations!
What a time to be alive - Congrats Doc
Károly is a Doctor now? Congrats man!
Merlin Kater watch the previous video.
hes not the kind with access to any mind altering drugs
I was thinking the same thing
Oh, you got your doctor's degree? Congratulations!
You are very kind, thank you. So happy!
Someone call a doctor.. My A.I. has just choked on a paper jam.
@@opendstudio7141 silly AI, I think you need to teach it to hold onto its papers better
@@king999art WHAT A TIME TO BE ALIVE!
Use AI to generate random stories
Use AI to generate photos from stories
Use AI to generate videos from photos
Use AI to upscale and make it 60fps
An AI movie?
Oh yes please damn i want to see that frankenstein monster of a movie yes please
There's already a movie whose screenplay was generated by AI, then fully acted out.
But that was a few years back, prior to transformers.
I wonder how things have progressed since then.
Yes!
Hahaha
Already has AI music, not recommend thou :))
First time I heard you say "Doctor". It rolled off your tongue smoothly! Beautiful! Congrats on your achievement! You have always been a PhD to us all.
Computer, enhance! *Rotate in 3D!*
"Now, show the perpetrator who was standing behind the corner!"
Stop! Enhamce that reflection on Epstein's glasses!... Yes! I think we got... Her???
Risto Paasivirta 😂😂
Just casually dropping that "Doctor"...
It's OK, I'd do it too.
3:21 Mini Cooper. Meenee cupper. Mghig eroigoig.
Luke Faulkner www
Bird. birb. bew.
You're totally rocking that doctor title.
3:20 Interesting that those cars ended up with completely different headlights compared to the source photos. Wonder why that would be.
Looks like it generates most common car model, the same for the bird.
Disclaimer, I don't know much about any of this past a surface level. But I'd imagine it might be because of the input reference materials used to train or design the AI having more generic lights. The lights for the 3D render of the Mini Cooper look like it's from an old Volkswagen Golf, which would be an ideal candidate for hatchbacks because of it's lack of a distinct feature; it's a generic car. I'd imagine that's what happens to the birds too, the AI just pulls from the reference images used to train it, and applies it accordingly.
@@mustakeenbari_serena_silentium Right, but you'd think that it wouldn't change the parts that it CAN see, and just reconstruct the rest.
@@CGPacifica You're right, it can pull from the input itself since it can see it clearly, why pull from the resources?
I'm just thinking out loud but perhaps it recognized the input as a "vehicle" and knowing it's a vehicle means it has to be at least somewhat symmetrical. Maybe the lights, or just the features on the far side (the part it can't see) are too much for it to infer and so it just approximates it to reference material instead?
It looks like it's not so much converting a 2d image to 3d, but instead creating a 3d object from what it was trained on and using the 2d image as a source for basic features-- almost like if someone was describing the photo and someone else was creating a 3d object based on that description.
Doctor! Congratulations 🎉🎊🍾🎈
You missed a video. The video before had a funny moment in the intro
This low level of detail already has applications such as for making game prototypes, with more fidelity than whiteboxing, just step outside or walk a trail and capture stand in objects. OR and this is the big one just feed mockup images to it.
Every time I am amazed. Thanks for putting the time in these videos!
This explains visual perception to a high degree. It shows how people deconstruct the scene they're present in into objects, and it also shows why people can project their awareness into space and look at things from another perspective. It also explains out of body experiences.
Thanks doc! This is awesome. Also congratulations!
Doctor? Congratulations!!! Well done!!!
This channel has been a huge inspiration to me.
the only thing I don´t understand:
why does the AI image look so different in the same perspective as the input photo?
Couldn´t they project the texture from the camera perspective onto the model, so at least the visible part of the model looks better?
3:35
They could and it'd be trivial, but it's not what they're trying to do. For a commercial application, google earth for example, it'd be an obvious improvement, but I believe here they're trying to generate everything from scratch to create more generalised and comprehensive method. This method will eventually work just from word descriptors for example, while projection mapped approach wouldn't.
If we did that it would, essentially, be cheating. The point is to see what the neural network is "seeing" and how it builds the image. They will of course continue to make the image better until it can one day create a perfect photo realistic recreation.
What they ought to do is have AI 2 test the finished output of AI 1 against that original angle and either reward or punish AI 1 for better and worse versions of the same object.
This would train it on specific objects after which you'd have to train it on categories of things (e.g., 👎🐦 ➡ 👍🐦, 👎🦉➡ 👍🦉, 👎🦚 ➡ 👍🦚, 👎🦆 ➡ 👍🦆 ⏩ good bird)
What a time to be alive!
Did you notice he said "Doctor"... Congrats on your thesis dude
Wow it's amazing,
Thanks dr.
Cool work! A few thoughts:
A system like this is theoretically impossible if I want to use it for 'any ' object because a single input image does not contain enough information for the computer to know what the other sides look like or what 'thickness' anything has. The only reason why it does know here, is because it was trained on specific examples. If I were to get a random foreign object that was not in the training set, it would likely not be able to figure some things out.
Second, I doubt the resulting model is at all a starting point for a 3D artist. They can't just 'add in the details' like you say, because the mesh itself likely has poor edge loops and is 'unhandleable' or unoptimized for any sort of software to be able to work with it well. Same goes for the generated UV and texture. Nothing beats the modeling and unwrapping skills of a human, because they truly understand what makes the most sense to do. That car UV looked really inefficient for example, using 4 different spots for every wheel while that could have been one.
Still, it provides an interesting starting point and may venture into better applications in the future.
Congrats Karoly!!! You became a doctor!
Congratulations doctor.
This software is to die for!
Nice to hear that you now are doktor :)
Hope you keep up the good videos
Now THIS is scary, congrats on your Doctorate!
Did u just say doctor???????? Congratulations !!!!!!! May the papers be with you
Is it a similar technique used to create the 3D representation of a city in Google Maps?
Amazing video btw !
@@Crayphor I remember hearing how the developers behind Microsoft Flight Simulator 2020 used AI to help build the cities in that game from real maps. Something like this would probably have an even better effect. Perhaps, one day, we can have street view without the need for cars to 360-degree camera drives about the world.
Google uses photogrammetry technique.
I think Google Maps is using something based on the Category-specific Mesh Reconstruction method, or at least related to it. You usually see those jagged artifacts in Maps which appear in CMR.
@@Crayphor like trees!
Thank you all for the knowledge!
3:00
input - The dude she tells me not to worry about
CMR - Me
This one got me good. 😄
I think what I love so much about these videos is that you can see that the technique is still not perfect, and that in the future we will likely look back on this and scoff, but still see it as one of many stepping stones in computer generation. You get the sense you’re watching history in the making.
Congrats on finishing your Ph. D, Károly!! Hoping to publish soon, as well! You're an inspiration!!
You can still improve this neural network if it distinguishes between objects and represents their approximate geometry in space. In addition, she can determine where the wheel or any other part should be located, then color the delati and assemble the whole machine separately, if the low-poly modeling from one object, then determine at what point the glass is, etc. as a result of the photograph, she will be able to collect all that she knows from the details inside the space. Cars, animals, people
Congratulations Doctor!
Congratulations Doctor ;)
The hummingbird example -clearly this is a bird, so object detection / categorisation would tell the net that it needed two wings as its a bird! Humans can fill in the details becuase they would know that a bird has two wings and it is in flight so any different angle is going to show the birds wings... 2 more papers...
no one:
me reading "ours": *soviet music starts playing*
Congratulations, doctor. 🎉🎊
This technique has been around for over a decade! I saw a documentary called CSI NY back in 2009 where an investigator zoomed, rotated and enhanced a photo using a computer program like this.
If you added a classification/recognition + modification neural network, to this one, it should get pretty close to perfect.
Whatever features the AI is unsure of, it can just assume that its like other birds that it knows about, like humans do.
When human beings turn 2D into 3D, we fill in whatever we dont know with what we expect. I.e. when were looking at that bird, wed automatically fill in specific details about feather, head shape etc, that we normally see in birds.
This would serve as a huge bonus for not only robots but anything relating to entertainment which would be especially modeling for video games and movies. Really awesome to see.
This opens up many many possibilities.
This thing is revolutionary!!
Congrats Doctor :D
Doctor! Very much compliments! Congratulations!⭐⭐⭐🎊🎉🥳
The first thing I thought of when I watched this was that this algorithm could be part of a network that watches old movies, creates models of all the objects in a scene and then renders an enhanced version of the same footage. We 've already seen RNNs that can ensure temporal coherence and physics based simulations that would provide enough information to perhaps even enhance footage considered to be damaged beyond repair. Is it very far fetched to think that a similar approach might be able to do the same with audio? I 'm absolutely fascinated and can 't wait to see someone try that.
If I was a police officer looking at those 3d car models the blurry CMR looked more accurate to shapes and branding styles of the car manufacturer than the newer one. It was hard to tell what brand car the newer one was making.
DOCTOR Károly Zsolnai-Fehér! What a time to be alive!
what a time to be alive!!! i love this so much!
That's amazing!!! Creating PS1 level graphics from a single photograph?!? That's insane! How would the results look if we took a synthesized image from previous works and fed it into this one? Would it end up with several artifacts? And if so, could we train a network to search for artifacts and further refine the generation process of Both of the generating networks?
Neural Networks are so cool 😲.
Got to study them 😅
Congratulations on Doctor! What a time to be alive!
congrats doc!
This is one of those things that is incredibly technically difficult but can't always be appreciated. People who aren't aware of what goes into those renders might look at them and think "wow, that's incredibly fuzzy and inaccurate. It barely even looks like a _____!" But I'm very glad to be able to appreciate this technology with the others watching :)
TMP: "Hold onto your papers!"
Also TMP: *Rotates SCP 173 on a platter*
*3D artists* : *damn dem machines , dey took 'er jobs*
Cheers for what you are doing doc!!!!
2:34 ... that's something
The name "Dr Kàroly Zsolnai-Fehér" just sounds right. Good addition to the intro.
It's pretty impressive generating the 3D, but it looks like it would be better off projecting the photo onto the mesh after that.
i did that once with a 3d face mesh inferred from photo( using vrn ) and you are absolutely right, projected texture result looked much better
Whoa! What's up doc? 😂 Look at you grow!
This system is a huge step for automation, so autonomous driving systems can generate occluded or far sides of objects the way our minds do automatically!
Congratulations on your successful thesis defense. I'm sure it was a long hard road, and it's amazing to see you set off on your new journey.
I imagine this could be improved by an algorithm that mirrors the objects, since most natural and man-made objects are symmetrical. If the network could somehow find the mirror of least resistance (most overlap with mirror object) in the least destructive way, then small faults like single wings could be filtered out.
You're a doctor? Awesome congrats man!
Dr! Congratulations!!!
what if it's given 3 or more pictures?
would greatly improve the photogrammetry process.
Yeah, I'm definitely thinking this will enhance photogrammetry for the time being, rather than outright replacing it. It'll probably make scanning reflective objects and parsing out shadows a lot easier, so objects wouldn't have to ideally be shot in diffuse ambient light to extract textures and minimize redlections.
it would make sense, of course there would be a lot of complexity to overcome. Conceptually you should be able to use trigonometry on 3 separate images to make a really accurate model, especially if the program knew the exact angle the picture was taken from.
As somebody who has reimplemented this paper for a dissertation, it does (usually) already use multiple views during training for better 3D comprehension. This is not mandatory, but tends to help a lot with the 3D shape
THE TIME HAS FINALLY COME FOR 3D
Two more papers down the line™ and it can include physics on those objects
Maybe this could improve street view when transitioning from a position to the next.
I bet that in 10 years from now we will be able to watch all our favorite movies in complete 3D reconstruction in real time, having the original movie as reference only. In 20 years from now, we will enjoy them as hologram projection movies.
Neural Rendering Engines are coming soon I imagine. A few years down the line and making a 3D scene might be as simple as describing it in plain text, with maybe a few example images.
Legit gonna happen. Hold on to your papers!
Maybe giving the network the 3D axis and light direction can help a lot, or even make a miror of one face of the object to replicate so the final model is actually simetrical. I feel like a few features can greatly improve the end result
Congratulations Dr. Zsolnai-Fehér!
Happy new PhD.
Thank you.
And what a paper!
I can imagine those robots in streets in very close future. Like 10 years from now.
Imagine retraining the network with two slightly differently displaced images just like your eyes!
0:35 *cries in aphantasia*
Can relate :(
Love the new "Hold on to your papers" icon
This is such a nice and curious community you gathered here! Congratulation on creating something socially significant in the digital age with digital measures :)
Any big progress since this video? (2 years old). Very impressive
Me: "Mom can we get bird"
Mom: "We have bird at home"
Bird at home: 2:35
HUGE GAME CHANGER!
WOW!!!!!!!!!
never in my wildest dreams did I think we would get to this point. I knew we would though, however, because I do believe the human brain is a deterministic machine. Therefore anyting the human brain can do, can be done in computer hardware. I had no idea we were going to be this far along at this early of a stage!!!
in a very real sense, the main takeaway from this particular neural network capability isn't so much the 3D extraction, amazing though that is, as much as it is a proof of the deterministic nature of our own neural networks. and I am taking great liberty with the term neural networks as it applies to the human brain. The simulation of neural networks within computers is only an approximation of the deterministic machine we have between our ears.
I would imagine that this particular neural network has really raised some eyebrows in the philosophical community and I believe it could be used as a very strong argument for the deterministic nature of the brain. There are some philosophers that believe we are non-deterministic, and that there is some metaphysical operations at work. Shear and pure fantasy and wishful thinking!
I remember there was a time when philosophers pointed to chess as being unsolvable by computers as a proof for the non-deterministic nature of the human brain. no matter how far they move the goalposts, the computers keep catching up. It's like the gaps logic in religion, it continuously gets eroded by technology. This is a fine example of that erosion.
This teases the possibility of finally having a useful robotic house cleaner. Especially with iterative feedback from the actual environment into the planner.
how do you generate training data for this? do you have pics of the thing from different angles?
I love not understanding anything, but still being surprised by stuff like this.
This is what i´ve been looking forward to!.
what I'm getting out of this is that the pixel 7's big stand out features going to be making 3d interactive worlds out of single images. Which you can send to your friends to play with on whatever google rebrands Stadia into. VR is going to get so dope.
I don't think I'll get sick of hearing Dr. Haha I pretty much yell screamed "YAS!!" on the first video. You earned the shit out of that PHD!
thankyou doctor.. now teach us more about A.I more.. because we are curious..!
From now on, if I ever hear someone saying "hey dude did you see the last Two Minute Papers video, you know, with Károly Zsolnai-Fehér?"
I will shout, "DOCTOR GODDAMMIT DOCTOR!"
okay DOCTOR!
This is gonna be so useful in game
Congrats, Dr. Zsolnai-feher!🤍
This would be great for game development.
Hoping for some software 4 papers down the line 👌🏻
I wonder if you could use a library of existing 3d models, then use image analysis to work out what is in the image, and find a relavent 3d model to use as a starting point
_What are some example applications in the real world?_
*Mathematicians:* Aight, imma head out
Great vid Dr.!
That's a Game changer for the Industry!
Where did you find time to get your Ph.D. between making all these videos?
can it work with multiple images to increase the 3d model?
excellent Dr Zsolnai-Feher
Great< Thanks man for your sharing