Pop/Generative Art in Linescapes

At a glance, there are strong similarities between pop art and the contemporary generative art movement. Pop art focuses on accessible subjects from mass-produced sources like comics and advertisements. Generative algorithms can produce entire collections of artworks, and while the subjects of generative works may vary, the crypto ecosystem makes them very accessible to view and collect, so new images can rapidly become part of the public discourse. These similarities inspired my project Linescapes, released one year ago on fxhash.

Linescapes starts from a concept ubiquitous in generative art: flow fields. While there are many ways to utilize vector fields in a generative algorithm, the most straightforward and familiar is to allow particles to move through this field, tracing out their trajectories as they go. I took this starting point and explored what happens when an expanded set of rules are used to determine the particles’ movement. The result pushes the images in a comic-esque direction, with frenetic energy and angular forms. The bright, outlined shapes are inspired by contemporary artists like Murakami. A pop art mindset feels like it fits naturally into a generative approach, and I am pleased with how they came together in this work.

Thinking back on the project, I have come to the realization that while pop art and generative art share some similarities, in a way generative art is the inverse of pop art. Pop art starts by considering a familiar subject, then elevates it to transform it into art. On the other hand, generative art starts with an aesthetic vision, then makes that vision more accessible through the use of algorithm. This statement seems to be at odds with my essay on intent in generative art, where I suggested that the presence of the algorithm adds another layer between the viewer and the artist, which makes it more difficult for the viewer to understand the artist’s intentions by looking at the algorithm’s outputs. I still believe this to be true, but there is another level of understanding that exists in a generative work that may make it more accessible than a non-generative counterpart. Instead of trying to understand something about the artist’s intention, the viewer can simply appreciate a work of art as an aesthetic arrangement produced by a codified set of instructions, and does not need to delve any deeper to have a positive interaction with the piece. This effect is magnified when considering a set of outputs from the same work, where similarities can be identified as obvious focal points to digest. The popular hangup that many have about “not getting” abstract art doesn’t seem to have hampered the success of abstract generative works. The existence of this level of understanding is part of the barrier to an artist’s ability to communicate their intent, but if the artist’s intention is primarily to make something aesthetically pleasing, then the generative nature of a work can give it the impression of having more to understand about it than its visual appeal. If the algorithm itself is fundamentally interesting, then any kind of appealing visual representation of that algorithm will feel like a well-rounded work.

Basic coding concepts like iteration through a grid, recursive subdivision, or flow fields can be used to great effect when combined with thoughtful visual design, allowing an artist to explore many variations through a generative approach. In such a scenario, the different components of the algorithm serve as tools to produce a result, but they don’t add any interest to the work. If instead novel algorithms are created and combined for the sole purpose of producing aesthetic images, these tools ubiquitous in the digital age are elevated to the level of artistry. Perhaps this subtler truth is what prompted the connection between pop and generative art; in the same way that pop art elevates familiar images, generative art can elevate code.

While the iterations of Linescapes have a look reminiscent of pop art, the heart of the work lies in the code. Its flow field basis is presented front and center, but the algorithm is uniquely empowered to create a huge variety of trajectories. The result is forms both familiar, like squares and lines, but also novel, like starbursts and crumpled polygons. Furthermore, the work continues what I feel is a natural approach to generative art that I began exploring in Primordial, which presents related variations within a single output. There are many straightforward ways to incorporate randomness into a generative work, like selecting from a list of pre-defined traits. It requires care in algorithmic design to produce emergent features, and even more care to produce a set of related emergent features as in the “coherent” and “cohesive” outputs.

Part of the beauty of generative art is feeling only partially responsible for the final result of my work, since once I hit publish the results are out of my hands. Having grown as an artist over the past year, I am pleased to find Linescapes continues to provide room to explore.

Intent in Generative Art