๐ŸŽจ๐Ÿค– "I made 25 squiggly doodles with Perlin noise and now consider myself an Artistโ„ข. Marvel at my 'genius' as I find enlightenment in the 'refreshing' simplicity of code that anyone with a calculator watch could replicate. Check out my #GitHub to witness this 'innovative' chaos born from PhD procrastination. ๐Ÿ“ˆ๐Ÿ’ค"
https://sighack.com/post/getting-creative-with-perlin-noise-fields #artificialintelligence #generativeart #coding #creativity #PerlinNoise #HackerNews #ngated
Getting Creative with Perlin Noise Fields

How to make infinite design variations using Perlin noise fields, a simple generative algorithm.

Sighack
Getting Creative with Perlin Noise Fields

How to make infinite design variations using Perlin noise fields, a simple generative algorithm.

Sighack
Getting Creative with Perlin Noise Fields

How to make infinite design variations using Perlin noise fields, a simple generative algorithm.

Sighack

Tried making a new kind of noise.

this is the length being presented

Here is code
#glsl #graphics #perlinnoise

A crude attempt at a noisy brush #PerlinNoise
Find the sketch-a-day archives and tip jar at: https://abav.lugaralgum.com/sketch-a-day
Code for this sketch at: https://github.com/villares/sketch-a-day/tree/main/2026/sketch_2026_01_28 #Processing #Python #py5 #CreativeCoding
InfiniteDiffusion: Bridging Learned Fidelity and Procedural Utility for Open-World Terrain Generation

For decades, procedural worlds have been built on procedural noise functions such as Perlin noise, which are fast and infinite, yet fundamentally limited in realism and large-scale coherence. Conversely, diffusion models offer unprecedented fidelity but remain generally confined to bounded canvases. We introduce InfiniteDiffusion, a training-free algorithm that reformulates diffusion sampling for lazy and unbounded generation, bridging the fidelity of diffusion models with the properties that made procedural noise indispensable: seamless infinite extent, seed-consistency, and constant-time random access. To demonstrate the utility of this approach, we present Terrain Diffusion, a framework for learned procedural terrain generation with a procedural noise-like interface. Our framework outpaces orbital velocity by 9 times on a consumer GPU, enabling realistic terrain generation at interactive rates. We integrate a hierarchical stack of diffusion models to couple planetary context with local detail, a compact Laplacian encoding to stabilize outputs across Earth-scale dynamic ranges, and an open-source infinite-tensor framework for constant-memory manipulation of unbounded tensors. Together, these components position diffusion models as a practical foundation for the next generation of infinite virtual worlds.

arXiv.org
Today during class we built a small example showing #random vs. #PerlinNoise
#Processing #Python py5

How do you programmatically generate non-uniform, natural forms? Perlin noise is a foundational algorithm for this task.

This Silphium Design guide provides a technical walkthrough for using Perlin noise in JavaScript to create animated 3D mountain terrain. It covers 2D heightmaps, fractal noise (fBm), and 3D implementation.

A solid resource for generative art and digital biophilia.

Read the guide: https://silphiumdesign.com/natural-forms-mountain-animation-perlin-noise/

#JavaScript #PerlinNoise #GenerativeArt #ProcGen #p5js #Threejs

Entspannungsprogrammieren am Abend.

Perlin-Rauschen gibt es auch in hรถheren Dimensionen โ€“ hier animiert auf nur einem von 3 Parametern.

#javascript #perlinnoise #creativecoding