High-Res Neural Cellular Automata
Neural CAs model self-organizing pattern formation. Now they can generate patterns at HD resolution in real-time, enabled by turning each CA cell into a Neural Field. Try 3 demos: grow a pattern from a seed (and damage it, it heals), synthesize PBR textures that can regenerate, or create 3D textures like clouds.
High-Res Neural Cellular Automata launched on June 17, 2026 with 208 votes, #140 of 22,647 launches that month and more than 99% of that year's launches.
In plain words
High-Res Neural Cellular Automata uses neural fields to generate self-organizing patterns at HD resolution in real-time. The system models pattern formation similar to biological growth, with the ability to heal damage to generated patterns. Users can grow patterns from seeds, create physically-based rendering textures that regenerate themselves, or synthesize 3D textures like clouds. It is designed for researchers and creators working with generative patterns and procedural content.
written from the facts on this page · September 2026
From the sources
Neural Cellular Automata: From Cells to Pixels
When changing $(\Delta x, \Delta y)$, we automatically adjust the value of $\Delta t$ to avoid the Euler integration from overshooting. In the Isotropic mode, the scaling factors are forced to be equal $\Delta x = \Delta y$. Neural Cellular Automata (NCAs) are bio-inspired dynamical systems in which identical cells iteratively apply a learned local update rule to self-organize into complex patterns, exhibiting regeneration, robustness, and spontaneous dynamics. Despite their success in texture synthesis and morphogenesis, NCAs remain largely confined to low-resolution outputs. This limitation stems from (1) training time and memory requirements that grow quadratically with grid size, (2)…from cells2pixels.github.io
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