So sad that nobody thought it important to ELI5 whatever on earth "gaussian splatting" means, and how it's different than regular splatting (if there's such a thing), or regular video. To me the video looks like the figures have slightly rounder edges, that's all.
A shame that kid was slept on. Allegedly (according to discord) he abandoned this because so many artists reached out to have him do this style of mv, instead of wanting to collaborate on music.
> many viewers focused on the chaos, the motion, and the unmistakable early MTV energy of the piece
It certainly moves around a lot!
It certainly looks like the tech and art style here are indissociable. Not only the use of Gaussian Splats made such extreme camera movement possible, one can be argued that it made them necessary.
Pause the video and notice the blurriness and general lack of details. But the frantic motion doesn't let the viewer focus on this details, most of them hidden by a copious amount of motion blur anyways.
To me it is typical of demos, both as in the "demoscene" and "tech demo" sense, where the art style is driven by the technology, insisting on what it enables, while at the same time working around its shortcomings. I don't consider it a bad thing of course, it leads to a lot of creativity and interesting art styles.
I'm David Rhodes, Co-founder of CG Nomads, developer of GSOPs (Gaussian Splatting Operators) for SideFX Houdini. GSOPs was used in combination with OTOY OctaneRender to produce this music video.
If you're interested in the technology and its capabilities, learn more at https://www.cgnomads.com/ or AMA.
I’m fascinated by the aesthetic of this technique. I remember early versions that were completely glitched out and presented 3d clouds of noise and fragments to traverse through. I’m curious if you have any thoughts about creatively ‘abusing’ this tech? Perhaps misaligning things somehow or using some wrong inputs.
Additionally, you can intentionally introduce view-dependent ghosting artifacts. In other words, if you take images from a certain angle that contain an object, and remove that object for other views, it can produce a lenticular/holographic effect.
Y'all did such a good job with this. It captivated HN and was the top post for the entire day, and will probably last for much of tomorrow.
If you don't know already, you need to leverage this. HN is one of the biggest channels of engineers and venture capitalists on the internet. It's almost pure signal (minus some grumpy engineer grumblings - we're a grouchy lot sometimes).
Post your contract info here. You might get business inquiries. If you've got any special software or process in what you do, there might be "venture scale" business opportunities that come your way. Certainly clients, but potentially much more.
(I'd certainly like to get in touch!)
--
edit: Since I'm commenting here, I'll expand on my thoughts. I've been rate limited all day long, and I don't know if I can post another response.
I believe volumetric is going to be huge for creative work in the coming years.
Gaussian splats are a huge improvement over point clouds and NeRFs in terms of accessibility and rendering, but the field has so many potential ways to evolve.
I was always in love with Intel's "volume", but it was impractical [1, 2] and got shut down. Their demos are still impressive, especially from an equipment POV, but A$AP Rocky's music video is technically superior.
During the pandemic, to get over my lack of in-person filmmaking, I wrote Unreal Engine shaders to combine the output of several Kinect point clouds [3] to build my own lightweight version inspired by what Intel was doing. The VGA resolution of consumer volumetric hardware was a pain and I was faced with fpga solutions for higher real time resolution, or going 100% offline.
World Labs and Apple are doing exciting work with image-to-Gaussian models [4, 5], and World Labs created the fantastic Spark library [6] for viewing them.
I've been leveraging splats to do controllable image gen and video generation [7], where they're extremely useful for consistent sets and props between shots.
I think the next steps for Gaussian splats are good editing tools, segmenting, physics, etc. The generative models are showing a lot of promise too. The Hunyuan team is supposedly working on a generative Gaussian model.
I remember splatting being introduced as a way to capture real life scenes, but one of the links you have provided in this discusson seems to have used a traditional polygon mesh scene as training input for the splat model. How common is this and why would one do it that way over e.g. vertex shader effects that give the mesh a splatty aesthetic?
I can see that relighting is still a work in progress, as the virtual spot lights tends to look flat and fake.
I understand that you are just making brighter splats that fall inside the spotlight cone and darker the ones behind lots of splats.
Do you know if there are plans for gaussian splats to capture unlit albedo, roughness and metalness? So we can relight in a more realistic manner?
Also, environment radiosity doesnt seem to translate to the splats, am I right?
Can such plugin be possible for Davinci Resolve, to have merge of scene captured from two iPhones with spatial data, into 3D scene?
With M4 that shouldn’t be problem?
I have. Personally, I'm a big fan of hybrid representations like this. An underlying mesh helps with relighting, deformation, and effective editing operations (a mesh is a sparse node graph for an otherwise unstructured set of data).
However, surface-based constraints can prevent thin surfaces (hair/fur) from reconstructing as well as vanilla 3DGS. It might also inhibit certain reflections and transparency from being reconstructed as accurately.
We (Evercoast) used 56 RealSense D455s. Our software can run with any camera input, from depth cameras to machine vision to cinema REDs. But for this, RealSense did the job. The higher end the camera, the more expensive and time consuming everything is. We have a cloud platform to scale rendering, but it’s still overall more costly (time and money) to use high res. We’ve worked hard to make even low res data look awesome. And if you look at the aesthetic of the video (90s MTV), we didn’t need 4K/6K/8K renders.
This is a great question, would love some some feedback on this.
I assume they stuck with realsense for proper depth maps. However, those are both limited to a 6 meters range, and their depth imaging isn't able to resolve features smaller than their native resolution allows (gets worse after 3m too, as there is less and less parallax among other issues). I wonder how they approached that as well.
Couldn’t you just use iphone pros for this?
I developed an app specifically for photogrammetry capture using AR and the depth sensor as it seemed like a cheap alternative.
EDIT:
I realize a phone is not on the same level as a red camera, but i just saw iphones as a massively cheaper option to alternatives in the field i worked in.
ASAP Rocky has a fervent fanbase who's been anticipating this album. So I'm assuming that whatever record label he's signed to gave him the budget.
And when I think back to another iconic hip hop (iconic that genre) video where they used practical effects and military helicopters chasing speedboats in the waters off of Santa Monica...I bet they had change to spear.
A single camera only captures the side of the object facing the camera. Knowing how far away that camera facing side of a Rubik's Cube help if you were making educated guesses(novel view synthesis), but it won't solve the problem of actually photographing the backside.
There are usually six sides on a cube, which means you need minimum six iPhone around an object to capture all sides of it to be able to then freely move around it. You might as well seek open-source alternatives than relying on Apple surprise boxes for that.
In cases where your subject would be static, such as it being a building, then you can wave around a single iPhone for the same effect for a result comparable to more expensive rigs, of course.
Edit: As I'm digging, this seems to be focused on stereoscopic video as opposed to actual point clouds. It appears applications like cinematic mode use a monocular depth map, and their lidar outputs raw point cloud data.
A LIDAR point cloud from a single point of view is a mono-ocular depth map. Unless the LIDAR in question is like, using supernova level gamma rays or neutrino generators for the laser part to get density and albedo volumetric data for its whole distance range.
You just can't see the back of a thing by knowing the shape of the front side with current technologies.
Recording pointclouds over time i guess i mean. I’m not going to pretend to understand video compression, but could it be possible to do the following movement aspect in 3d the same as 2d?
My friend and colleague shared a link with me. Pretty cool to see this trending here. I'm very passionate about Gaussian splatting and developing tools for creatives.
Hah, for the past day, I've been trying to somehow submit the Helicopter music video / album as a whole to HN. Glad someone figured out the angle was Gaussian.
r/playboicarti is one of my favorite places to go to just turn my brain off and see shitposts that have a certain reminiscence to me, almost a "high school class when the teacher didn't show up" vibe.
Because expertise, love, and care cut across all human endeavor, and noticing those things across domains can be a life affirming kind of shared experience.
Favorited. This will be a timeless comment for me, and will remind some perspective to appreciate things I might not be otherwise familiar with, and thereby care about.
Not everything has to be. Sometimes, an artist's style or a particular track just hits a particular vibe one may be after or need in a particular moment.
I'm not a fan of this music either but I could imagine hearing it while I'm studying or coding.
Don't trash something just because it's not your vibe. Not everything has to be Mozart.
I mean, it's not like I trashed it or compared it to Mozart—I even made sure to include "interesting, stimulating, or tonally remarkable" in an attempt to preempt that latter pushback.
But even if I did, why can't I? It's fine to call some music shit. Just like you can call my opinion shit.
Policing dissenting opinions and saying everything is equally worthy of praise are two sides of the same coin sliding in the vending machine that sells us the sad state of affairs we live in today.
You absolutely trashed it in your first sneering, shitty swipe about “culture”. You don’t get to make comments like that and then whine about “policing” like a four year-old caught in the cookie jar.
Super cool to read but can someone eli5 what Gaussian splatting is (and/or radiance fields?) specifically to how the article talks about it finally being "mature enough"? What's changed that this is now possible?
1. Create a point cloud from a scene (either via lidar, or via photogrammetry from multiple images)
2. Replace each point of the point cloud with a fuzzy ellipsoid, that has a bunch of parameters for its position + size + orientation + view-dependent color (via spherical harmonics up to some low order)
3. If you render these ellipsoids using a differentiable renderer, then you can subtract the resulting image from the ground truth (i.e. your original photos), and calculate the partial derivatives of the error with respect to each of the millions of ellipsoid parameters that you fed into the renderer.
4. Now you can run gradient descent using the differentiable renderer, which makes your fuzzy ellipsoids converge to something closely reproducing the ground truth images (from multiple angles).
5. Since the ellipsoids started at the 3D point cloud's positions, the 3D structure of the scene will likely be preserved during gradient descent, thus the resulting scene will support novel camera angles with plausible-looking results.
ELI5 has meant friendly simplified explanations (not responses aimed at literal five-year-olds) since forever, at least on the subreddit where the concept originated.
Now, perhaps referring to differentiability isn't layperson-accessible, but this is HN after all. I found it to be the perfect degree of simplification personally.
If one actually tried to explain to a five year old, they can use things like analogy, simile, metaphor, and other forms of rhetoric. This was just a straight-up technical explanation.
Lol. Def not for 5 year olds but it's about exactly what I needed
How about this:
Take a lot of pictures of a scene from different angles, do some crazy math, and then you can later pretend to zoom and pan the camera around however you want
How hard is it to handle cases where the starting positions of ellipsoids in 3D is not correct (being too off). How common is such a scenario with the state of the art? E.g., if having only a stereoscopic image pair, the correspondences are often not accurate.
Gaussian splatting is a way to record 3-dimensional video. You capture a scene from many angles simultaneously and then combine all of those into a single representation. Ideally, that representation is good enough that you can then, post-production, simulate camera angles you didn't originally record.
For example, the camera orbits around the performers in this music video are difficult to imagine in real space. Even if you could pull it off using robotic motion control arms, it would require that the entire choreography is fixed in place before filming. This video clearly takes advantage of being able to direct whatever camera motion the artist wanted in the 3d virtual space of the final composed scene.
To do this, the representation needs to estimate the radiance field, i.e. the amount and color of light visible at every point in your 3d volume, viewed from every angle. It's not possible to do this at high resolution by breaking that space up into voxels, those scale badly, O(n^3). You could attempt to guess at some mesh geometry and paint textures on to it compatible with the camera views, but that's difficult to automate.
Gaussian splatting estimates these radiance fields by assuming that the radiance is build from millions of fuzzy, colored balls positioned, stretched, and rotated in space. These are the Gaussian splats.
Once you have that representation, constructing a novel camera angle is as simple as positioning and angling your virtual camera and then recording the colors and positions of all the splats that are visible.
It turns out that this approach is pretty amenable to techniques similar to modern deep learning. You basically train the positions/shapes/rotations of the splats via gradient descent. It's mostly been explored in research labs but lately production-oriented tools have been built for popular 3d motion graphics tools like Houdini, making it more available.
Thanks for the explanation! It makes a lot of sense that voxels would scale as badly as they do, especially if you want to increase resolution.
Am I right in assuming that the reason this scales a lot better is because the Gaussian splats, once there's enough "resolution" of them, can provide the estimates for how light works reasonably well at most distances? What I'm getting at is, if I can see Gaussian splats vs voxels similarly to pixels vs vector graphics in images?
It’s a point cloud where each point is a semitransparent blob that can have a view dependent color: color changes depending on direction you look at them. Allowing to capture reflections, iridescence…
You generate the point clouds from multiple images of a scene or an object and some machine learning magic
I think this tech has become "production-ready" recently due to a combination of research progress (the seminal paper was published in 2023 https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/) and improvements to differentiable programming libraries (e.g. PyTorch) and GPU hardware.
For the ELI5, Gaussian splatting represents the scene as millions of tiny, blurry colored blobs in 3D space and renders by quickly "splatting" them onto the screen, making it much faster than computing an image by querying a neural net model like radiance fields.
I found this VFX breakdown of the recent Superman movie to have a great explanation of what it is and what it makes possible: https://youtu.be/eyAVWH61R8E?t=232
tl;dr eli5: Instead of capturing spots of color as they would appear to a camera, they capture spots of color and where they exist in the world. By combining multiple cameras doing this, you can make a 3D works from footage that you can then zoom a virtual camera round.
Oh wow, somehow I was not aware of how capable this technology has become, looks like a major game changer, across many fields.
In the near term, it could be very useful for sports replays. The UFC has this thing where they stitch together sequences of images from cameras all around the ring, to capture a few seconds of '360 degree' video of important moments. It looks horrible, this would be a huge improvement.
Really amazing video. Unfortunately this article is like 60% over my head. Regardless, I actually love reading jargon-filled statements like this that are totally normal to the initiated but are completely inscrutable to outsiders.
"That data was then brought into Houdini, where the post production team used CG Nomads GSOPs for manipulation and sequencing, and OTOY’s OctaneRender for final rendering. Thanks to this combination, the production team was also able to relight the splats."
Hi, I'm one of the creators of GSOPs for SideFX Houdini.
The gist is that Gaussian splats can replicate reality quite effectively with many 3D ellipsoids (stored as a type of point cloud). Houdini is software that excels at manipulating vast numbers of points, and renderers (such as Octane) can now leverage this type of data to integrate with traditional computer graphics primitives, lights, and techniques.
Can you put "Gaussing splats" in some kind of real world metaphor so I can understand what it means? Either that or explain why "Gaussian" and why "splat".
I am vaguely aware of stuff like Gaussian blur on Photoshop. But I never really knew what it does.
Gaussian splatting is a bit like photogrammetry. That is, you can record video or take photos of an object or environment from many angles and reproduce it in 3D. Gaussians have the capability to "fade" their opacity based on a Gaussian distribution. This allows them to blend together in a seamless fashion.
The splatting process is achieved by using gradient descent from each camera/image pair to optimize these ellipsoids (Gaussians) such that the reproduce the original inputs as closely as possible. Given enough imagery and sufficient camera alignment, performed using Structure from Motion, you can faithfully reproduce the entire space.
I think this means that you could produce more versions of this music video from other points of view without having to shoot the video again. For example, the drone-like effects could take a different path through the scene. Or you could move people/objects around and still get the lighting right.
Given where this technology is today, you could imagine 5-10 years from now people will watch live sports on TV, but with their own individual virtual drone that lets them view the field from almost any point.
> I am vaguely aware of stuff like Gaussian blur on Photoshop. But I never really knew what it does.
Blurring is a convolution or filter operation. You take a small patch of image (5x5 pixels) and you convolve it with another fixed matrix, called a kernel. Convolution says multiply element-wise and sum. You replace the center pixel with the result.
https://en.wikipedia.org/wiki/Box_blur is the simplest kernel - all ones, and divide by the kernel size. Every pixel becomes the average of itself and its neighbors, which looks blurry. Gaussian blur is calculated in an identical way, but the matrix elements follow the "height" of a 2D Gaussian with some amplitude. It results in a bit more smoothing as farther pixels have less influence. Bigger the kernel, more blurrier the result.There are a lot of these basic operations:
Happily. Gaussian splats are a technique for 3D images, related to point clouds. They do the same job (take a 3D capture of reality and generate pictures later from any point of view "close enough" to the original).
The key idea is that instead of a bunch of points, it stores a bunch of semi-transparent blobs - or "splats". The transparency increases quickly with distance, following a normal distribution- also known as the "Gaussian distribution."
How can you expect someone to tailor a custom explanation, when they don’t know your level of mathematical understanding, or even your level of curiosity. You don’t know what a Gaussian blur does; do you know what a Gaussian is? How deeply do you want to understand?
If you’re curious start with the Wikipedia article and use an LLM to help you understand the parts that don’t make sense. Or just ask the LLM to provide a summary at the desired level of detail.
> How can you expect someone to tailor a custom explanation, when they don’t know your level of mathematical understanding, or even your level of curiosity.
My bad! I am the author. Gaussian splatting allows you to take a series of normal 2D images or a video and reconstruct very lifelike 3D from it. It’s a type of radiance field, like NeRFs or voxel based methods like Plenoxels!
Hello! I’m Chris Rutledge, the post EP / cg supervisor at Grin Machine. Happy to answer any questions. Glad people are enjoying this video, was so fun to get to play with this technique and help break it into some mainstream production
Awesome work, incredibly well done! What was the process like for setting the direction on use of these techniques with Rakim? Were you basically just trusted to make something great or did they have a lot of opinions on the technicalities?
To be honest it looks like it was rendered in an old version of Unreal Engine. That may be an intentional choice - I wonder how realistic guassian splatting can look? Can you redo lights, shadows, remove or move parts of the scene, while preserving the original fidelity and realism?
The way TV/movie production is going (record 100s of hours of footage from multiple angles and edit it all in post) I wonder if this is the end state. Gaussian splatting for the humans and green screens for the rest?
The aesthetic here is at least partially an intentional choice to lean into the artifacts produced by Gaussian splatting, particularly dynamic (4DGS) splatting. There is temporal inconsistency when capturing performances like this, which are exacerbated by relighting.
That said, the technology is rapidly advancing and this type of volumetric capture is definitely sticking around.
Knowing what I know about the artist in this video this was probably more about the novelty of the technology and the creative freedom it offers rather than it is budget.
For me it felt more like higher detail version of Teardown, the voxel-based 3d demolition game. Sure it's splats and not voxels, but the camera and the lighting give this strong voxel game vibe.
Several of ASAP's video have a lo-fi retro vibe, or specific effects such as simulating stuff like a mpeg a/v corruption, check out A$AP Mob - Yamborghini High (https://www.youtube.com/watch?v=tt7gP_IW-1w)
I guess the technology still has some quality limitations, otherwise we would already see it in mainstream movies, e.g. to simulate smooth camera motions beyond what is achievable with video stabilization. It's much more difficult to achieve 4K quality that holds up on a movie theater screen, without visible artifacts, than to do an artistic music video.
Tangential, but I've been exploring gaussian splatting as a photographic/artistic medium for a while, and love the expressionistic quality of the model output when deprived of data.
Thanks! I'm using the KIRI Engine in Blender to render splats from my photos (https://github.com/Kiri-Innovation/3dgs-render-blender-addon) and then process the image as I would my photography in Lightroom. There are lots of different photogrammetry tools for generating plys (the point cloud) like PolyCam (https://poly.cam).
Be sure to watch the video itself* - it’s really a great piece of work. The energy is frenetic and it’s got this beautiful balance of surrealism from the effects and groundedness from the human performances.
* (Mute it if you don’t like the music, just like the rest of us will if you complain about the music)
Similarly, the music video for Taylor Swif[0] (another track by A$AP Rocky) is just as surrealistic and weird in the best way possible, but with an eastern european flavor of it (which is obviously intentional and makes sense, given the filming location and being very on-the-nose with the theme).
I can see how this kind of videos can attract the tiktok addicts with less than 3 seconds of attention time.
I wonder what will be the state of cinema/series/video clips in 30 years? Will singers/rappers give up sentences completely and just mention names of emojis? Will we have to use screens at 576hz to be able to watch acclerated videos without seeing a constant blur?
I guess most kids from today would fall asleep before the end of the generic of Twin Peaks or the opening scene of Fargo.
It’s interesting to see Gaussian splatting show up in a mainstream music video this quickly. A year ago it was mostly a research demo, and now artists are using it as part of their visual toolkit.
What I find most notable is how well splats handle chaotic motion like helicopter shots — it’s one of the few 3D reconstruction methods that doesn’t completely fall apart with fast movement.
Feels like we’re going to see a lot more of this in creative work before it shows up in anything “serious”.
The end result is really interesting. As others have pointed out, it looks sort of like it was rendered by an early 2000s game engine. There’s a cohesiveness to the art direction that you just can’t get from green screens and the like. In service of some of the worst music made by human brains, but still really cool tech.
I was wondering how these kind of scenes were done in the movie Enter The Void back in 2009. Maybe they used different technique but looks very similar: https://vimeo.com/60239415
I can't really respect the artist though, after the assault on a random bystander in Stockholm in 2019 — for which he was convicted. He got off too easy.
This reminds me about how Soulja Boy just used a cracked copy of Fruity Loops and a cheap microphone and recorded all his songs that made him millions.[1]
Edit: Ok this was a big team of VFX producers who did this. Still, prices are coming down dramatically in general, but yeah that idea is a bit of an underfit to this case.
You might consider why this article which has nothing to do with AI as you know it (except for the machine learning aspects of Gaussian splatting), and was produced by a huge team of vfx professionals, has made you think about AI democratising culture (despite the fact that music videos and films have been cheap to make for decades). Don’t just look for opportunities to discuss your favourite talking points.
I think in 2026 it's hard to make a video look this "bad" without it being a clear aesthetic choice, so not sure you could find this video in another setting.
I really disagree with the label brainrot. Brainrot is low-quality garbage with no artistic merit, and very little thought behind its creation, which does nothing but make you briefly pause while scrolling, before scrolling away with no lasting impression being done to your mind (besides increased boredom and inability to focus).
This is clearly an artistic statement, whether you like the art or not. A ton of thought and time was put into it. And people will likely be thinking and discussing this video for some time to come.
A$ap Rocky's music videos have some really good examples of how AI can be used creatively and not just to generate slop. My favorite is Taylor Swif, it's a super fun video to watch.
The texture of Gaussian Splatting always looks off to me. It looks like the entire scene has been textured or has a bad, uniform film grain filter to me. Everything looks a little off in an unpleasing way -- things that should be sharp are aren't, and things that should be blurry are not. It's uncanny valley and not in a good way. I don't get what all the rage is about it and it always looks like really poor B-roll to me.
How did Rhianna look him in the eyes and say "yes babe, good album, release it, this is what the people wanted after 7 years, it is pleasing to listen to and enjoyable"?
the real question is how much of the art is their own and how much is outside expectations and their reactions to it.
And it's not always giving in to those voices, sometimes it's going in the opposite direction specifically to subvert those voices and expectations even if that ends up going against your initial instincts as an artist.
With someone like A$AP Rocky, there is a lot of money on the line wrt the record execs but even small indie artists playing to only a hundred people a night have to contend with audience expectation and how that can exert an influence on their creativity.
Im sure it was more like, “hey babe, can I get a few millions to go in the studio and experiment/make some art?” And then she was like, “yeah go for it! Make some weird shit.”
If I was in his position I’d probably be doing the same. Why bother with another top hit that pleases the masses.
so basically despite the higher resource requirements like 10TB of data for 30 minutes of footage, the compositing is so much faster and more flexible and those resources can be deleted or moved to long term storage in the cloud very quickly and the project can move on
fascinating
I wouldn't have normally read this and watched the video, but my Claude sessions were already executing a plan
the tl;dr is that all the actors were scanned in a 3D point cloud system and then "NeRF"'d which means to extrapolate any missing data about their transposed 3D model
this was then more easily placed into the video than trying to compose and place 2D actors layer by layer
Gaussian splatting is not NeRF (neural radiance field), but it is a type of radiance field, and supports novel view synthesis. The difference is in an explicit point cloud representation (Gaussian splatting), versus a process that needs to be inferred by a neural network.
Hmm if gaussian splatting is radiance field rendering then so is any 3D rendering, and what's the point of using the name? Though having looked up the name it seems like it isn't well defined enough to mean much anyway tbh.
> One recurring reaction to the video has been confusion. Viewers assume the imagery is AI-generated. According to Evercoast, that couldn’t be further from the truth. Every stunt, every swing, every fall was physically performed and captured in real space. What makes it feel synthetic is the freedom volumetric capture affords.
Pretty sure most of this could be filmed with a camera drone and preprogrammed flight path...
Did the Gaussian splatting actually make it any cheaper? Especially considering that it needed 50+ fixed camera angles to splat properly, and extensive post-processing work both computationally and human labour, a camera drone just seems easier.
> Pretty sure most of this could be filmed with a camera drone and preprogrammed flight path
This is a “Dropbox is just ftp and rsync” level comment. There’s a shot in there where Rocky is sitting on top of the spinning blades of a helicopter and the camera smoothly transitions from flying around the room to solidly rotating along with the blades, so it’s fixed relative to rocky. Not only would programming a camera drone to follow this path be extremely difficult (and wouldn’t look as good), but just setting up the stunt would be cost prohibitive.
This is just one example of the hundreds you could come up with.
Drones and 2d compositing could do a lot. They would excel in some areas used in the video, require far more resources than this technique in others, and be completely infeasible on a few.
They would look much better in a very "familiar" way. They would have much less of the glitch and dynamic aesthetic that makes this so novel.
If it was achievable, cheaper, and of equal quality then it would have been done that way. Surely it would’ve been done that way a long time ago too. Drone paths have been around a lot longer than this technology.
There’s no proof of your claim and this video is proof of the opposite.