3D will be back but not as we know it
CC Attribution - NoDerivatives 2.0 UK: England & Wales:
You are free to use, copy, distribute and transmit the work or content in unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
MacMillan, Tim
GoPro launched the “Dual­Hero 2.0” stereo rig in 2014. This offered amazing sync ability (pixel level), low cost and high resolution (17:9 2.7K@30p ­ which looks amazing when viewed on a 4K 3D monitor). But the consumer stereo 3D market had already crashed. 3D continues to be a strong attraction at the cinema because the viewing experience is carefully controlled. This same challenge is now plainly visible in the emerging VR, AR and MR technologies, but there are really compelling reasons why 3D, either as stereo output or depth­map will play an essential role in the coming video technologies. 
 The next talk is 3-way with back but not as we know it. So the author is Mr. Tim MacMillan. Yes. From GoPro. Thank you. Hi everybody, Tim MacMillan. So this is going to be a little bit different to what you've been looking at already. I'm from GoPro. Obviously, you know, I hope you know who GoPro is. We manufacture small but very high quality, very powerful, generally called sports cameras. And I joined GoPro four years ago. I don't know if they quite knew why they hired me but they put me in a department, which is the Department of One, which was me, called Multicamera. And what I was working on was ways of getting GoPro's to work in concert. So Multicamera means you don't use one camera, use two, four, six, twelve, ten, you know, any number. But there are certain things you have to do to make them work together. Because we understood that there is, there are new technologies coming which will require capture from multiple cameras, not just single monocular views but multiple views. And this includes everything from stereo to light field. So I'm going to talk a little bit about that. So, so I did a little bit of a historical context. I've been working in the multicamera field for 30 years building camera arrays. I just wanted to show that cameras have been around since before cinema, before video. This is Edward Mybridge back in the 1870s. And over there was a French photographer called Nadar who did what was called sculptural photography, which was basically, I think it was 12 or 24 cameras with synchronized exposures. So they could capture somebody sitting there and then use those photographs to create a sculpture. It would have to be carved by hand, but there we are. And jump to the present day. Here we are. And you see, we're still doing the same thing. We have the linear progressive arrays, like made famous by the matrix and stuff like that. And we have something new appearing now, which is a volumetric capture. And hang on to that right-hand image. I'll show you something in a bit. So what is volumetric capture? So yes, it could be one of many things at the moment. So this is an example of a 5 gigapixel volumetric capture. This is just a single frozen moment. The motion is actually the motion of the virtual camera. And what we have here is a volumetric capture and then creation of a virtual camera movement in Z-space in front of the camera array. So the cameras were all positioned away from the high jumper. But for the actual production shot, we're able to in Z-space move the camera in towards the subject to put the camera essentially where you could never put a camera. This is one of the amazing powers of camera array and computational imaging. So the technique we're using in this is basically photogrammetry, where we, and it's pure image based photogrammetry. We're not using any time of flight, laser, or any other kind of depth sensing. This is all done from computational imaging. And this capture, like I said, was 5 gigapixels. And you could have actually pushed the camera in further until you see the pores of her skin. There's that much detail in this. And every single strand of hair is clearly defined as well. But 50 gigapixels is a lot of data. And I'm sure this took several days to render. But this was the last Olympics, so a while back. So Z-space is easy to understand. You have a camera array, which could be doing photogrammetry, could be there for light field, could be there for like a matrix style thing. We just go frame by frame along. But because you've got the light rays coming into each camera, you can choose any position in front of there. And you'll have all the data from these cameras here. You'll have the data to create the image viewed from the Z-space camera. So this is quite a dense linear array, much denser than is practical in reality. The other thing to talk about is resolution threshold. Because as you move your camera in Z-space away from your array, you're going to lose resolution as well. But what we can do is use optical flow and some techniques of interpolation to actually bring the resolution back a bit. The downside of photogrammetry is that you have to render a 3D object and then refilm it. So it's a kind of two-step process to produce the image. And this works equally well for stereoscopic output. And in fact, about 10 years ago, the company... I'm with GoPro. I'm also CEO of Time Slice Films, which is a company that specializes in camera array. We actually did some work for some 3D stereo movies. One was Street Dance, I think. Kind of hip hop Street Dance movie. But we actually were able to generate very convincing Z-space camera in 3D stereo. Because as you change your convergence, you can just interpolate back... You project back onto your original array here and you can generate... You have all your data there to generate not only your position in Z, but also your convergence as well. So that was very exciting. But like I said, that's kind of old stuff. So that's an example of a linear array, maybe a moderately dense array. We have now emerging a new type. Obviously, you've seen a lot of interest recently in spherical cameras. A lot of companies are trying to produce spherical cameras to produce new virtual augmented reality. But there's issues with that. Not least of which is you immediately see how low your pixel density is going to be. You're having to cover a much wider field with fewer cameras. So there are issues there. So there's a lot of people kind of working on ideas for this. I thought I'd show this slide. This is a slide that Paul DeBevec at USC put up last year. We had a visual effects society light field conference. The first light field conference held at Paul's lab at USC. And Leitrow were there trying to plug their new rebirth as an augmented reality company. But Paul said, why use Leitrow sensors when you can just put a bunch of go-pros in a ball? It's much simpler and probably much cheaper as well. But he calculated that basically you need a shed load of cameras. I think in America they might say a shit ton of cameras to do this. Again, there is a resolution threshold here. For these algorithms to work, you have to have enough resolution. So this is a huge problem when it comes to capture. And then you have to encode that somehow. And then you have to share it. How do you share something like that? And then how do you view it? Well, how do you view it is partly your problem. But there's a lot of unanswered questions at the minute. So the thing is, where do we start with all this? And good old GoPro is actually quietly kind of tiptoeing into this field. So this is the Odyssey camera. This is a rig, an array, which GoPro is bringing out this year. We're actually releasing this this year. And this was in conjunction with GoPro's Jump project. So the Jump project is, are you familiar with Google Cardboard? It's a small viewer, a stereoscopic viewer for your phone. They want to put those in schools all over the world and create a library of content that school children can see. So someone in Africa can see what an iceberg looks like. And someone in Italy can see what it looks like in San Francisco and yada yada, you know, all that stuff. And they needed a rig to do that. And they figured they needed 16 cameras in a ring. But the interesting thing is, this is the first kind of computational imaging device, multi-camera computational imaging device that is actually going to be on the market and people out there using it. And I know that a lot of people are going to use this for other things than doing the Google Jump pictures. And the image this kicks out, you know, the image that Google produces for this is really quite huge. It's an 8K over under. I've written that equirectangular. It's not, I think, it's a square. It's an 8K by 8K square with two 4K by 8K windows of over under with the panoramic scene. So it's not a full sphere. It's a panorama. It's a 120 degrees vertical field of view. And what you do with this, you go out, you shoot stuff, you acquire the content on the cards, you upload it to the Google Cloud, the huge black box that is floating in the sky, that is Google's processing engine, and eventually they send you back your finished stereo 3D video. So that's what I'm calling the divergent array. So that's where we're working at the moment to produce content for divergent array. There's another type of array, again, which is beginning to appear, the convergent array. This gives us a very dense pixel map. This is interesting because this enables it to actually more easily create useful content using computational processes. It gives us the maximum chance of locating voxels. I'm sure you know what voxels are, they're pixels with volumetric pixels. This type of array is scalable. You could generate and create an array to do something on your tabletop, or it could encompass the whole stadium. You see various people. There was the iVision during the Super Bowl recently, where they had like a 30 cameras around the roof of the Super Bowl stadium, and they're able to do some sort of frozen time moves, camera moves around that. But obviously, the smaller the volume you're capturing, the higher the resolution you can capture at the course of this resolution issue to do with distance. We have a choice of outputs, light field, photogrammetric, optical flow. But actually, the photogrammetry pipelines are now beginning to come to maturity. This is a little test we did at Time Slice recently. I'm going to just come out of that and go into this so that I can show you this. Oops. A little bit difficult to... Oops, I've disappeared. Come back, come back. Let me just stop. Let me just go to another one. If in doubt, have a spare one. Here we are. All right. There's some weird lag going on with this. It's both laggy and hypersensitive. All right. So this is just 30 frames of me, but this is a volumetric image. We call it volumetric video. We can call it free viewpoint media, or we can call it 4D video. I mean, all those names are being sort of banded around at the moment. This is actually... This is going to go live on Facebook tomorrow, I believe. Let me just pause that and see if I can just get a little bit closer. Come on. That'll do. That'll do. All right. So this is... So the render engine for this is polygon-based. It's not a light field. It's not a point cloud. Although those types of engines are coming. The trouble there is that there are no capture pipelines or processing pipelines for light field yet, but CloudPoint will be coming quite soon. And now we're going to flip back to our talk. Please. Yes. Sometimes it just works. There we are. So the thing about volumetric capture... Volumetric capture is, I think, going to be crucial to the evolution of both still and moving image. And it fundamentally changes visual content creation from a fixed viewpoint to a free viewpoint. And this is going to have a lot of ramifications in terms of how people want to view images. And I guess this is... I'll get to this point at the end, but it's going to affect you guys a lot. Right. So... Okay. So how will this become mainstream? Here's a couple of screenshots from Microsoft's last PR video about HoloLens. And what they're showing is this 4D video, this free viewpoint content, the volumetric video. And they're kind of hedging their bets. In their PR stuff, they have both a screen that you could... With your HoloLens, you can put anywhere you like. And if the content allows it, that could be 3D stereo, or it could be 2D. Or the choice could be yours. Or if the content is this free viewpoint type content, then instead of watching a 2D or a 3D stereo display, you can watch a volumetric display. So you take your football match and you put it on your table and you watch it, as if you were in the audience. And nothing that, you can watch it as if you're in the audience or as if you're in the field with the players. You can have put your viewpoint anywhere you like. So there are other issues to do with this as well. I know this from the research we're doing with spherical cameras, is that there are huge problems to do with if you have a spherical view and you've got a HMD, and you're stuck in the nodal point of the sphere. So when you move your head, the whole world moves with you. This is very bad. It's very bad for your brain and your nausea and everything else. It causes huge issues. So people that are working in AR and VR and everything else desperately need some volumetric ability to move within a volume. And hence, Lightfield now beginning to appear as a capture process. And there's another more important reason why image capture will have depth information, which is that it's not just about looking at it, it's not just about consuming it. It's also the fact that depth information will be incredibly useful and maybe the primary driver for depth information in imaging. Because the depth information can be used in Google's 3D map of the world in Streetview. And if you're looking at kind of second screen content, you can use the depth information from cameras within the stadium at the time to generate the 3D model of the stadium. So I would say that we're replacing cinematic language of vicarious experience with a first person view. I'd ask you guys to think about, will people really want to look at stereo 3D monitors if you can look at it as a volume? I look at the history of 3D and I see this, it's been hampered by the constraints of the monitor. And there's been a lot of research into why the stereo 3D market crashed in the consumer space. It still lives on in the cinema space. But here we've got capture coming with the power to give you a 3D, a volumetric object, a video object, which maybe, I'm not saying it will be, I'm saying it's a good chance that may be actually more compelling than a 3D monitor. But people haven't learned how to work with this content yet. You see the attempts people have to take a spherical camera, put it in a concert and expect it to be immersive. It's not immersive. I'm sitting there looking around. I can't move my head. My head's fixed and I can't move around. When people are able to move around within the concert and experience it for themselves, their own kind of exploration of that environment, then it will be interesting. So it's going to change from something which is preordained. This is a screen. It's edited. This is what you're going to see. We're going to lead you on a journey to a journey which is self-generated. But the good news is that in terms of 3D monitors, the content will hopefully be inherently 3D. You'll be able to present it as 3D stereo or light field or point cloud, whatever you like. Okay. That's it. I'll end with 16 is the magic number. Any idea why 16 is the magic number? What can you do with 16 cameras other than a circle? Any ideas? Like 2 by 8 or 4 by 4. There are many other configurations you can put these cameras in. So people have been very interested in this array because they can reconfigure it to start experimenting with light field arrays and stuff like that. So that's it. Thank you. Okay. Thank you. Thank you. Thank you.
 The next talk is 3-way with back but not as we know it.  So the author is Mr. Tim MacMillan.  Yes.  From GoPro.  Thank you.  Hi everybody, Tim MacMillan.  So this is going to be a little bit different to what you've been looking at already.  I'm from GoPro.  Obviously, you know, I hope you know who GoPro is.  We manufacture small but very high quality, very powerful, generally called sports cameras.  And I joined GoPro four years ago.  I don't know if they quite knew why they hired me but they put me in a department, which  is the Department of One, which was me, called Multicamera.  And what I was working on was ways of getting GoPro's to work in concert.  So Multicamera means you don't use one camera, use two, four, six, twelve, ten, you know,  any number.  But there are certain things you have to do to make them work together.  Because we understood that there is, there are new technologies coming which will require  capture from multiple cameras, not just single monocular views but multiple views.  And this includes everything from stereo to light field.  So I'm going to talk a little bit about that.  So, so I did a little bit of a historical context.  I've been working in the multicamera field for 30 years building camera arrays.  I just wanted to show that cameras have been around since before cinema, before video.  This is Edward Mybridge back in the 1870s.  And over there was a French photographer called Nadar who did what was called sculptural  photography, which was basically, I think it was 12 or 24 cameras with synchronized  exposures.  So they could capture somebody sitting there and then use those photographs to create a  sculpture.  It would have to be carved by hand, but there we are.  And jump to the present day.  Here we are.  And you see, we're still doing the same thing.  We have the linear progressive arrays, like made famous by the matrix and stuff like that.  And we have something new appearing now, which is a volumetric capture.  And hang on to that right-hand image.  I'll show you something in a bit.  So what is volumetric capture?  So yes, it could be one of many things at the moment.  So this is an example of a 5 gigapixel volumetric capture.  This is just a single frozen moment.  The motion is actually the motion of the virtual camera.  And what we have here is a volumetric capture and then creation of a virtual camera movement  in Z-space in front of the camera array.  So the cameras were all positioned away from the high jumper.  But for the actual production shot, we're able to in Z-space move the camera in towards  the subject to put the camera essentially where you could never put a camera.  This is one of the amazing powers of camera array and computational imaging.  So the technique we're using in this is basically photogrammetry, where we, and it's pure image  based photogrammetry.  We're not using any time of flight, laser, or any other kind of depth sensing.  This is all done from computational imaging.  And this capture, like I said, was 5 gigapixels.  And you could have actually pushed the camera in further until you see the pores of her  skin.  There's that much detail in this.  And every single strand of hair is clearly defined as well.  But 50 gigapixels is a lot of data.  And I'm sure this took several days to render.  But this was the last Olympics, so a while back.  So Z-space is easy to understand.  You have a camera array, which could be doing photogrammetry, could be there for light field,  could be there for like a matrix style thing.  We just go frame by frame along.  But because you've got the light rays coming into each camera, you can choose any position  in front of there.  And you'll have all the data from these cameras here.  You'll have the data to create the image viewed from the Z-space camera.  So this is quite a dense linear array, much denser than is practical in reality.  The other thing to talk about is resolution threshold.  Because as you move your camera in Z-space away from your array, you're going to lose  resolution as well.  But what we can do is use optical flow and some techniques of interpolation to actually  bring the resolution back a bit.  The downside of photogrammetry is that you have to render a 3D object and then refilm  it.  So it's a kind of two-step process to produce the image.  And this works equally well for stereoscopic output.  And in fact, about 10 years ago, the company...  I'm with GoPro.  I'm also CEO of Time Slice Films, which is a company that specializes in camera array.  We actually did some work for some 3D stereo movies.  One was Street Dance, I think.  Kind of hip hop Street Dance movie.  But we actually were able to generate very convincing Z-space camera in 3D stereo.  Because as you change your convergence, you can just interpolate back...  You project back onto your original array here and you can generate...  You have all your data there to generate not only your position in Z, but also your convergence  as well.  So that was very exciting.  But like I said, that's kind of old stuff.  So that's an example of a linear array, maybe a moderately dense array.  We have now emerging a new type.  Obviously, you've seen a lot of interest recently in spherical cameras.  A lot of companies are trying to produce spherical cameras to produce new virtual augmented reality.  But there's issues with that.  Not least of which is you immediately see how low your pixel density is going to be.  You're having to cover a much wider field with fewer cameras.  So there are issues there.  So there's a lot of people kind of working on ideas for this.  I thought I'd show this slide.  This is a slide that Paul DeBevec at USC put up last year.  We had a visual effects society light field conference.  The first light field conference held at Paul's lab at USC.  And Leitrow were there trying to plug their new rebirth as an augmented reality company.  But Paul said, why use Leitrow sensors when you can just put a bunch of go-pros in a ball?  It's much simpler and probably much cheaper as well.  But he calculated that basically you need a shed load of cameras.  I think in America they might say a shit ton of cameras to do this.  Again, there is a resolution threshold here.  For these algorithms to work, you have to have enough resolution.  So this is a huge problem when it comes to capture.  And then you have to encode that somehow.  And then you have to share it.  How do you share something like that?  And then how do you view it?  Well, how do you view it is partly your problem.  But there's a lot of unanswered questions at the minute.  So the thing is, where do we start with all this?  And good old GoPro is actually quietly kind of tiptoeing into this field.  So this is the Odyssey camera.  This is a rig, an array, which GoPro is bringing out this year.  We're actually releasing this this year.  And this was in conjunction with GoPro's Jump project.  So the Jump project is, are you familiar with Google Cardboard?  It's a small viewer, a stereoscopic viewer for your phone.  They want to put those in schools all over the world and create a library of content  that school children can see.  So someone in Africa can see what an iceberg looks like.  And someone in Italy can see what it looks like in San Francisco and yada yada, you  know, all that stuff.  And they needed a rig to do that.  And they figured they needed 16 cameras in a ring.  But the interesting thing is, this is the first kind of computational imaging device,  multi-camera computational imaging device that is actually going to be on the market  and people out there using it.  And I know that a lot of people are going to use this for other things than doing the  Google Jump pictures.  And the image this kicks out, you know, the image that Google produces for this is really  quite huge.  It's an 8K over under.  I've written that equirectangular.  It's not, I think, it's a square.  It's an 8K by 8K square with two 4K by 8K windows of over under with the panoramic scene.  So it's not a full sphere.  It's a panorama.  It's a 120 degrees vertical field of view.  And what you do with this, you go out, you shoot stuff, you acquire the content on the  cards, you upload it to the Google Cloud, the huge black box that is floating in the  sky, that is Google's processing engine, and eventually they send you back your finished  stereo 3D video.  So that's what I'm calling the divergent array.  So that's where we're working at the moment to produce content for divergent array.  There's another type of array, again, which is beginning to appear, the convergent array.  This gives us a very dense pixel map.  This is interesting because this enables it to actually more easily create useful content  using computational processes.  It gives us the maximum chance of locating voxels.  I'm sure you know what voxels are, they're pixels with volumetric pixels.  This type of array is scalable.  You could generate and create an array to do something on your tabletop, or it could  encompass the whole stadium.  You see various people.  There was the iVision during the Super Bowl recently, where they had like a 30 cameras  around the roof of the Super Bowl stadium, and they're able to do some sort of frozen  time moves, camera moves around that.  But obviously, the smaller the volume you're capturing, the higher the resolution you can  capture at the course of this resolution issue to do with distance.  We have a choice of outputs, light field, photogrammetric, optical flow.  But actually, the photogrammetry pipelines are now beginning to come to maturity.  This is a little test we did at Time Slice recently.  I'm going to just come out of that and go into this so that I can show you this.  Oops.  A little bit difficult to...  Oops, I've disappeared.  Come back, come back.  Let me just stop.  Let me just go to another one.  If in doubt, have a spare one.  Here we are.  All right.  There's some weird lag going on with this.  It's both laggy and hypersensitive.  All right.  So this is just 30 frames of me, but this is a volumetric image.  We call it volumetric video.  We can call it free viewpoint media, or we can call it 4D video.  I mean, all those names are being sort of banded around at the moment.  This is actually...  This is going to go live on Facebook tomorrow, I believe.  Let me just pause that and see if I can just get a little bit closer.  Come on.  That'll do.  That'll do.  All right.  So this is...  So the render engine for this is polygon-based.  It's not a light field.  It's not a point cloud.  Although those types of engines are coming.  The trouble there is that there are no capture pipelines or processing pipelines for light  field yet, but CloudPoint will be coming quite soon.  And now we're going to flip back to our talk.  Please.  Yes.  Sometimes it just works.  There we are.  So the thing about volumetric capture...  Volumetric capture is, I think, going to be crucial to the evolution of both still and  moving image.  And it fundamentally changes visual content creation from a fixed viewpoint to a free  viewpoint.  And this is going to have a lot of ramifications in terms of how people want to view images.  And I guess this is...  I'll get to this point at the end, but it's going to affect you guys a lot.  Right.  So...  Okay.  So how will this become mainstream?  Here's a couple of screenshots from Microsoft's last PR video about HoloLens.  And what they're showing is this 4D video, this free viewpoint content, the volumetric  video.  And they're kind of hedging their bets.  In their PR stuff, they have both a screen that you could...  With your HoloLens, you can put anywhere you like.  And if the content allows it, that could be 3D stereo, or it could be 2D.  Or the choice could be yours.  Or if the content is this free viewpoint type content, then instead of watching a 2D or  a 3D stereo display, you can watch a volumetric display.  So you take your football match and you put it on your table and you watch it, as if you  were in the audience.  And nothing that, you can watch it as if you're in the audience or as if you're in the field  with the players.  You can have put your viewpoint anywhere you like.  So there are other issues to do with this as well.  I know this from the research we're doing with spherical cameras, is that there are huge  problems to do with if you have a spherical view and you've got a HMD, and you're stuck  in the nodal point of the sphere.  So when you move your head, the whole world moves with you.  This is very bad.  It's very bad for your brain and your nausea and everything else.  It causes huge issues.  So people that are working in AR and VR and everything else desperately need some volumetric  ability to move within a volume.  And hence, Lightfield now beginning to appear as a capture process.  And there's another more important reason why image capture will have depth information,  which is that it's not just about looking at it, it's not just about consuming it.  It's also the fact that depth information will be incredibly useful and maybe the primary  driver for depth information in imaging.  Because the depth information can be used in Google's 3D map of the world in Streetview.  And if you're looking at kind of second screen content, you can use the depth information  from cameras within the stadium at the time to generate the 3D model of the stadium.  So I would say that we're replacing cinematic language of vicarious experience with a first  person view.  I'd ask you guys to think about, will people really want to look at stereo 3D monitors  if you can look at it as a volume?  I look at the history of 3D and I see this, it's been hampered by the constraints of the  monitor.  And there's been a lot of research into why the stereo 3D market crashed in the consumer  space.  It still lives on in the cinema space.  But here we've got capture coming with the power to give you a 3D, a volumetric object,  a video object, which maybe, I'm not saying it will be, I'm saying it's a good chance  that may be actually more compelling than a 3D monitor.  But people haven't learned how to work with this content yet.  You see the attempts people have to take a spherical camera, put it in a concert and  expect it to be immersive.  It's not immersive.  I'm sitting there looking around.  I can't move my head.  My head's fixed and I can't move around.  When people are able to move around within the concert and experience it for themselves,  their own kind of exploration of that environment, then it will be interesting.  So it's going to change from something which is preordained.  This is a screen.  It's edited.  This is what you're going to see.  We're going to lead you on a journey to a journey which is self-generated.  But the good news is that in terms of 3D monitors, the content will hopefully be inherently 3D.  You'll be able to present it as 3D stereo or light field or point cloud, whatever you  like.  Okay.  That's it.  I'll end with 16 is the magic number.  Any idea why 16 is the magic number?  What can you do with 16 cameras other than a circle?  Any ideas?  Like 2 by 8 or 4 by 4.  There are many other configurations you can put these cameras in.  So people have been very interested in this array because they can reconfigure it to start  experimenting with light field arrays and stuff like that.  So that's it.  Thank you.  Okay.  Thank you.  Thank you.  Thank you.
SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_0_1211.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_1212_3347.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_3348_4199.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_4200_4259.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_4260_4571.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_4572_4775.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_4776_4835.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_4836_4895.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_4896_5147.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_5148_5207.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_5208_5471.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_5472_5531.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_5532_5855.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_5856_6107.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_6108_6167.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_6168_6431.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_6432_6491.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_6492_11495.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_11496_17855.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_17856_18203.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_18204_18503.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_18504_19463.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_19464_19823.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_19824_20447.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_20448_22475.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_22476_23315.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_23316_30287.png SD_A_2016__3D_will_be_back_but_not_as_we_know_it_m1KuWcCd8nU_30288_34969.png