Show HN: High-Res Neural Cellular Automata
Researchers have developed a new method for Neural Cellular Automata (NCA) that allows for high-resolution output by combining coarse grid evolution with an implicit decoder. This approach overcomes previous limitations regarding memory usage and computational demands in texture synthesis and morphogenesis.
Why it matters
This advancement enables more efficient and scalable AI-driven generative modeling for complex visual patterns.
Neural Cellular Automata: From Cells to Pixels Ehsan Pajouheshgar 1 , Yitao Xu 1 , Ali Abbasi 1* , Alexander Mordvintsev 2 , Wenzel Jakob 1 , Sabine Süsstrunk 1 1 EPFL 2 Google Research * Work done during internship at EPFL SIGGRAPH 2026 arXiv GitHub Texture Demo --> PBR Texture Demo Growing Demo --> Growing Demo 3D Texture Demo GitHub (Coming Soon) --> --> --> --> --> Font Awesome fontawesome.com - >--> --> Colab --> --> --> --> --> --> --> --> Supplementary --> --> --> --> --> --> --> Steps / Frame: 1/2x Brush Size LPPN Scale: x4 Brush Mode Click or tap the canvas to interact with the NCA! 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 $\Delta x = \Delta y$.
The article is a technical summary of a research paper.
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