Converging AI techniques with Analog & Digital Photography
After beginning his career in film photography in the late 1990s and early 2000s, Charlie has continually embraced new visual and storytelling techniques as they have emerged. From film to digital capture, video, aerial drone, and most recently computer generation, he views these mediums as tools to be used holistically and often in conjunction with one another, depending on the task at hand. The decisions about which tools to implement are shaped not only by immediate creative needs but also by the broader, ongoing flux in how society perceives art, history, psychology, and politics at any given moment. Ultimately, visual tools are only as strong as the intention behind them and the discernment to understand which techniques best address a particular problem or desired outcome.
The burgeoning field of computer-generated imagery is evolving monthly. Preconceived notions about how content is captured and constructed continue to shift, and certain methods prove more effective than others as these processes mature. Creating useful, quality work frequently involves combining traditionally captured material with generated elements, then iterating, refining, and translating media across specialized systems alongside established editing software, resulting in a layered, intricate, and deeply considered creative process.
Artificial intelligence enables still images to be translated into motion, offering significant new creative and cost-saving potential when strategically integrated into the planning process. Beyond creative and economic efficiencies, photography retains a distinct advantage over traditional video capture in specific product-focused applications. High-resolution still cameras produce large image files with greater detail, tonal range, and information depth, enabling more precise rendering and manipulation of the product. This enhanced fidelity can be particularly valuable in commercial contexts where clarity, texture, and material nuance are essential. Images can be extended into short ambient video clips or linked together to create longer-form video narratives. While deceptively simple, high-quality creation in this emerging media format involves new and often hybrid techniques for visual capture and still frequently requires original content creation. Furthermore, it requires the running or processing of media to move between multiple specialized computational programs, as well as traditional media editing programs.
In certain cases, AI can generate new still or moving images from a single piece of source material or synthesize entirely new compositions by combining multiple media inputs. The process, however, is far from automatic and, though steadily improving, still has limitations. New capture methodologies are often required to ensure that the appropriate types and depth of data are gathered, demanding foresight, technical planning, and a working understanding of light, perspective, texture, motion, and spatial relationships. Workflows frequently involve iterative testing, running, and refining media across specialized computational systems in tandem with traditional post-production tools, resulting not simply in generated imagery but in human-directed visual construction shaped by both technological capability and artistic intention.