Show HN: High-Res Neural Cellular Automata

# Neural Cellular Automata: From Cells to Pixels Source: [https://cells2pixels.github.io/](https://cells2pixels.github.io/)  The NCA operates on a coarse lattice of cells \(in this example vertices of a mesh\)\.**Center:**A sampling point \\\(\\Point\\\) \(red dot\) inside a triangle primitive, whose vertices correspond to NCA cells \\\(\\State\_i,\\,\\State\_j,\\,\\State\_k\\\)\. The*local coordinate*\\\(u\(\\Point\)\\\) expresses the point’s position inside the primitive, while the*locally averaged cell state*\\\(\\bar\{\\State\}\(\\Point\)\\\) is obtained by interpolating the surrounding cell states\.**Right:**The Local Pattern Producing Network \(**LPPN**\), A shared lightweight MLP, receives \\\(\(\\bar\{\\State\}\(\\Point\), u\(\\Point\)\)\\\) as input and outputs the target properties, such as color and surface normal, at point \\\(\\Point\\\)\. The NCA and the LPPN are trained jointly and end\-to\-end\. Play with the interactive visualization below to see coarse NCA cell states and the output the LPPN generates\.