  Recently, a range of neural network-based methods for image rendering have
been introduced. One such widely-researched neural radiance field (NeRF) relies
on a neural network to represent 3D scenes, allowing for realistic view
synthesis from a small number of 2D images. However, most NeRF models are
constrained by long training and inference times. In comparison, Gaussian
Splatting (GS) is a novel, state-of-the-art technique for rendering points in a
3D scene by approximating their contribution to image pixels through Gaussian
distributions, warranting fast training and swift, real-time rendering. A
drawback of GS is the absence of a well-defined approach for its conditioning
due to the necessity to condition several hundred thousand Gaussian components.
To solve this, we introduce the Gaussian Mesh Splatting (GaMeS) model, which
allows modification of Gaussian components in a similar way as meshes. We
parameterize each Gaussian component by the vertices of the mesh face.
Furthermore, our model needs mesh initialization on input or estimated mesh
during training. We also define Gaussian splats solely based on their location
on the mesh, allowing for automatic adjustments in position, scale, and
rotation during animation. As a result, we obtain a real-time rendering of
editable GS.
