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.
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.
Try GSOPs yourself: https://github.com/cgnomads/GSOPs (example content included).
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.
You're right that you can intentionally under-construct your scenes. These can create a dream-like effect.
It's also possible to stylize your Gaussian splats to produce NPR effects. Check out David Lisser's amazing work: https://davidlisser.co.uk/Surface-Tension.
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.
(I'm not the author.)
You can train your own splats using Brush or OpenSplat
How did you find out this was posted here?
Also, great work!
And thank you!
>Evercoast deployed a 56 camera RGB-D array
Do you know which depth cameras they used?
So likely RealSense D455.
I recommend asking https://www.linkedin.com/in/benschwartzxr/ for accuracy.
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.
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.
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.
But yes, you can easily use iPhones for this now.
Check this project, for example: https://zju3dv.github.io/freetimegs/
Unfortunately, these formats are currently closed behind cloud processing so adoption is a rather low.
Before Gaussian splatting, textured mesh caches would be used for volumetric video (e.g. Alembic geometry).
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.
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.
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.
You generate the point clouds from multiple images of a scene or an object and some machine learning magic
I'm not up on how things have changed recently
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.
"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."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.
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.
Read more here: https://towardsdatascience.com/a-comprehensive-overview-of-g....
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:
https://en.wikipedia.org/wiki/Kernel_(image_processing)
If you see "Gaussian", it implies the distribution is used somewhere in the process, but splatting and image kernels are very different operations.
For what it's worth I don't think the Wikipedia article on Gaussian Blur is particularly accessible.
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.
The other two replies did a pretty good job!
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?
That said, the technology is rapidly advancing and this type of volumetric capture is definitely sticking around.
The quality can also be really good, especially for static environments: https://www.linkedin.com/posts/christoph-schindelar-79515351....
* (Mute it if you don’t like the music, just like the rest of us will if you complain about the music)
Seems like a really cool technology, though.
I wonder if anyone else got the same response, or it's just me.
I’m curious what other artists end up making with it.
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.
No, it’s simply the framerate.
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.
I don’t disagree with you—I felt “Tailor Swif,” “DMB,” and “Both Eyes Closed” were all stronger than the tracks that made it onto this album.
But sometimes you’ve gotta ship the project in the state it’s in and move on with your life.
Maybe now he can move forward and start working on something new. And perhaps that project will be stronger.
If I was in his position I’d probably be doing the same. Why bother with another top hit that pleases the masses.
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
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.
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.
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.
There’s no proof of your claim and this video is proof of the opposite.
This approach is 100% flexible, and I'm sure at least part of the magic came from the process of play and experimentation in post.
Volumetric capture like this allows you to decide on the camera angles in post-production
This tech is moving along at breakneck pace and now we're all talking about it. A drone video wouldn't have done that.