Comment by tel

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.