Mesh Neural Cellular Automata

• Published in: ACM transactions on graphics 2024-07, Vol.43 (4), p.1-16, Article 122
• Main Authors: Pajouheshgar, Ehsan, Xu, Yitao, Mordvintsev, Alexander, Niklasson, Eyvind, Zhang, Tong, Süsstrunk, Sabine
• Format: Article
• Language: English
• Subjects: Appearance and texture representations; Artificial intelligence; Bio-inspired approaches; Computer graphics; Computer vision; Computer vision representations; Computing methodologies; Distributed artificial intelligence; Image manipulation; Machine learning; Machine learning approaches; Multi-agent systems; Parallel algorithms; Parallel computing methodologies; Self-organization; Texturing
• ISSN: 0730-0301; 1557-7368
• DOI: 10.1145/3658127

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/.