# Mesh Neural Cellular Automata arXiv: https://arxiv.org/abs/2311.02820 · HTML: https://mlsanity.com/paper/2311.02820/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Builds on 27 of its 62 references that are in the map. - First posted on arXiv 6 Nov 2023. ## Builds on (most cited first, 27 of 27) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014) - [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015) - [Text2Mesh: Text-Driven Neural Stylization for Meshes](https://mlsanity.com/paper/2112.03221/) (2021) - [TEXTure: Text-Guided Texturing of 3D Shapes](https://mlsanity.com/paper/2302.01721/) (2023) - [Text2Tex: Text-driven Texture Synthesis via Diffusion Models](https://mlsanity.com/paper/2303.11396/) (2023) - [Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer](https://mlsanity.com/paper/1908.01210/) (2019) - [Texture Fields: Learning Texture Representations in Function Space](https://mlsanity.com/paper/1905.07259/) (2019) - [Design Space for Graph Neural Networks](https://mlsanity.com/paper/2011.08843/) (2020) - [Style Transfer by Relaxed Optimal Transport and Self-Similarity](https://mlsanity.com/paper/1904.12785/) (2019) - [StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators](https://mlsanity.com/paper/2108.00946/) (2021) - [Single-Image SVBRDF Capture with a Rendering-Aware Deep Network](https://mlsanity.com/paper/1810.09718/) (2018) - [ReLU Fields: The Little Non-linearity That Could](https://mlsanity.com/paper/2205.10824/) (2022) - [TANGO: Text-driven Photorealistic and Robust 3D Stylization via Lighting Decomposition](https://mlsanity.com/paper/2210.11277/) (2022) - [Texturify: Generating Textures on 3D Shape Surfaces](https://mlsanity.com/paper/2204.02411/) (2022) - [Learning a Neural 3D Texture Space from 2D Exemplars](https://mlsanity.com/paper/1912.04158/) (2019) - [X-Mesh: Towards Fast and Accurate Text-driven 3D Stylization via Dynamic Textual Guidance](https://mlsanity.com/paper/2303.15764/) (2023) - [Learning Graph Cellular Automata](https://mlsanity.com/paper/2110.14237/) (2021) - [Mesh2Tex: Generating Mesh Textures from Image Queries](https://mlsanity.com/paper/2304.05868/) (2023) - [Generating Procedural Materials from Text or Image Prompts](https://mlsanity.com/paper/2304.13172/) (2023) - [GramGAN: Deep 3D Texture Synthesis From 2D Exemplars](https://mlsanity.com/paper/2006.16112/) (2020) - [DyNCA: Real-time Dynamic Texture Synthesis Using Neural Cellular Automata](https://mlsanity.com/paper/2211.11417/) (2022) - [Two-Stream Convolutional Networks for Dynamic Texture Synthesis](https://mlsanity.com/paper/1706.06982/) (2017) - [μNCA: Texture Generation with Ultra-Compact Neural Cellular Automata](https://mlsanity.com/paper/2111.13545/) (2021) - [On Demand Solid Texture Synthesis Using Deep 3D Networks](https://mlsanity.com/paper/2001.04528/) (2020) - [Differentiable Programming of Reaction-Diffusion Patterns](https://mlsanity.com/paper/2107.06862/) (2021) - [Texture Generation Using A Graph Generative Adversarial Network And Differentiable Rendering](https://mlsanity.com/paper/2206.08547/) (2022) ## Built on by (most cited first, 11 of 11) - [NoiseNCA: Noisy Seed Improves Spatio-Temporal Continuity of Neural Cellular Automata](https://mlsanity.com/paper/2404.06279/) (2024) - [Neural Cellular Automata: From Cells to Pixels](https://mlsanity.com/paper/2506.22899/) (2025) - [AdaNCA: Neural Cellular Automata As Adaptors For More Robust Vision Transformer](https://mlsanity.com/paper/2406.08298/) (2024) - [Neural Particle Automata: Learning Self-Organizing Particle Dynamics](https://mlsanity.com/paper/2601.16096/) (2026) - [Emergent Dynamics in Neural Cellular Automata](https://mlsanity.com/paper/2404.06406/) (2024) - [Neural Differential Appearance Equations](https://mlsanity.com/paper/2410.07128/) (2024) - [Future Research Avenues for Artificial Intelligence in Digital Gaming: An Exploratory Report](https://mlsanity.com/paper/2412.14085/) (2024) - [Missing Data Imputation using Neural Cellular Automata](https://mlsanity.com/paper/2509.00651/) (2025) - [Von Neumann Networks](https://mlsanity.com/paper/2605.05780/) (2026) - [Applications of Neural Cellular Automata: State of the Art, Challenges and Opportunities](https://mlsanity.com/paper/2609.24595/) (2026) - [DynaMesh: Dynamic 3D Texture Generation](https://mlsanity.com/paper/2610.05529/) (2026) ## Abstract (arXiv) 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.