  Texture modeling and synthesis are essential for enhancing the realism of
virtual environments. Methods that directly synthesize textures in 3D offer
distinct advantages to the UV-mapping-based methods as they can create seamless
textures and align more closely with the ways textures form in nature. We
propose Mesh Neural Cellular Automata (MeshNCA), a method that directly
synthesizes dynamic textures on 3D meshes without requiring any UV maps.
MeshNCA is a generalized type of cellular automata that can operate on a set of
cells arranged on non-grid structures such as the vertices of a 3D mesh.
MeshNCA accommodates multi-modal supervision and can be trained using different
targets such as images, text prompts, and motion vector fields. Only trained on
an Icosphere mesh, MeshNCA shows remarkable test-time generalization and can
synthesize textures on unseen meshes in real time. We conduct qualitative and
quantitative comparisons to demonstrate that MeshNCA outperforms other 3D
texture synthesis methods in terms of generalization and producing high-quality
textures. Moreover, we introduce a way of grafting trained MeshNCA instances,
enabling interpolation between textures. MeshNCA allows several user
interactions including texture density/orientation controls,
grafting/regenerate brushes, and motion speed/direction controls. Finally, we
implement the forward pass of our MeshNCA model using the WebGL shading
language and showcase our trained models in an online interactive demo, which
is accessible on personal computers and smartphones and is available at
https://meshnca.github.io.
