Interlocking Cable Ties, Generative NFTs, Mathematical Optimization Art
#034 - Creative Coding / Generative Arts Weekly
"The most beautiful thing we can experience is the mysterious. It is the source of all true art and all science. He to whom this emotion is a stranger, who can no longer pause to wonder and stand rapt in awe, is as good as dead: his eyes are closed." - Albert Einstein
This week has been fascinating on multiple fronts in the generative art space. This week we have seen a lot of interest in the NFT space. Not only have the generative NFTs minted through ArtBlocks.io been frequently sold out within minutes, but then within 24 hrs, we also find the secondary markets (a.k.a. OpenSea) flipping the artwork to be sold with a profit margin. Take, for example, the following stats from yesterday,
It's been exciting to see the interest of what generative art brings and the medium that I am a bit passionate about. I mean, I have been writing about it almost weekly for the last year, and where it is today and where it was then has been fascinating. Back in March, we were still trying to figure out what they were.
All that being said, I think we still have a lot of exploring to do, and I hope that our core community will continue to invest the energy necessary to push the boundaries. It’s wonderful to find the monetary love we receive from all the many supporters out there and continue to find exciting ways to use creative coding as a tool to discover, bring awe, and delight!
🖌️ Unconventional Media
Artist Sui Park (previously) zips together simple nylon cable ties to create sprawling biomorphic sculptures and site-specific installations that resemble heaving nighttime seas, prickly moss, and vibrant amorphous creatures. Park, who was born in Seoul and currently lives in New York, started hand-dying the uniform fasteners a few years ago to deepen the contrast between the mass-produced material and her spiky organic masses. “Each has a subtle difference in shape and angle, and when grouped and connected together to develop into a larger form, the subtlety creates a dynamic and a characteristic of my work,” she says.
📸 Generative Graphics
Triangula uses a modified genetic algorithm to triangulate or polygonate images. It works best with images smaller than 3000px and with fewer than 3000 points, typically producing an optimal result within a couple of minutes. For a full explanation of the algorithm, see this page in the wiki.
This project revisits the animated film Continuous Sound and Image Moments, made in 1966 by Jeffrey Shaw, Willem Breuker, and Tjebbe van Tijen. The original film consisted of a sequence of hand drawings, each shown for only a few seconds. In Latent Embeddings, a machine learning algorithm constructs a generative model from digitized versions of those drawings. New images are then produced by exploring the latent space of the model.
You will need to click the link above to see the view; it appears that it doesn’t play on other platforms.
The Aesthetics of Probability
A quick watch, but an interesting way of seeing how Refik Anadol starts working with the Quantum AI team at Google to create fascinating work.
🔖 Articles and Tutorials
Text-to-image generation involves the automatic production of images which somehow reflect a given word or phrase. Recently, a new approach to this task has become available, which employs generative adversarial networks (GANs) for image generation, with the latent vector inputs found by a backpropagation search. The search is directed by a loss function calculated by another pair of pre-trained models called CLIP, which can estimate how appropriately a generated image reflects a text prompt. We compare and contrast the usage of CLIP in guiding various GANs for artistic image production, highlighting the huge potential for this approach, as well as current limitations.
Here is the paper for more detail: https://arxiv.org/pdf/2102.01645.pdf
Artificial Intelligence Art: Questions Answered
Definitely check out the many questions that have been asked about artificial intelligence and art. It probably falls into the realm of generative art, but it is definitely a different head of the beast.
There is a stereotype of artists as strictly right-brained personalities. (Insert your favorite pop-culture image of a painter in a beret and a smock splotched with paint). This trope encourages an image of artists as people who are purely creative, unconcerned with practical matters. But in reality, making art requires a significant time investment both terms of tools and learning the peculiarities of a particular medium. To state the obvious, it takes a monumental amount of work to gain mastery at a craft, even if the ultimate purpose of that mastery is creative in nature. When the medium is pencil, an artist gains a mastery of the HB grading scale, and blending on top of the prerequisite background of contrast, perspective and composition. When the medium is code, the artist gains a mastery of writing code and graphics drivers and building shaders.
Shoutout to Alexi Andre, who works on the amazing creative coding package Nannou
wanted to do something similar, but for non-circular and image-based shapes. This post describes the method I came up with. Before I got started, I search around for an existing solution. I was pointed toward a 2D shape collision detection library that looks nice for rectangles, ellipses, and even 2D polygons - basically any shape you can draw in a canvas. @sabrleRaph also pointed me towards a neat solution for for polygon packing. But neither of these covered image-based shapes, so that wasn’t going to work. So here’s the solution I came up with. It runs a little slow and isn’t great for real-time results, but works well enough and is good for final images or GIFs. If you’re reading this, and know a better way, please let me know!
Computer graphics have been an interest of mine for as long as I’ve used a computer. The ability to create life like renderings has always held my captivation in one way or another. I grew up using open source graphics software like GIMP and later Blender3D. I fondly remember letting the family laptop run all night rendering frames for a 3D animation I had made in Blender for school. Being able to theoretically produce photorealistic images like Pixar or Lucasfilm made me feel like I could create my own reality, only given enough time and patience. Instead of going to school for animation, I hedged my bets on studying Computer Engineering, learning how the computer worked rather than learning how to use the suites of programs to their fullest. Throughout university I kept up with computer graphics by reading SIGGRAPH papers, watching the talks, and trying to implement some basic graphics programs myself.
Robert Bosch provides a lively and accessible introduction to the geometric, algebraic, and algorithmic foundations of optimization. He presents classical applications, such as the legendary Traveling Salesman Problem, and shows how to adapt them to make optimization art—opt art. Each chapter in this marvelously illustrated book begins with a problem or puzzle and demonstrates how the solution can be derived using a host of artistic methods and media, including 3D printing, laser cutting, and computer-controlled machining. Bosch focuses on mathematical modeling throughout—converting a problem into a workable mathematical form, solving it using optimization techniques, and examining the results, which can take the form of mosaics, line drawings, and even sculpture. All you need is some high-school algebra, geometry, and calculus to follow along.