The traditional way to render a realistic image is from a physics perspective: provide a complete enough description of the scene and simulate light propagation and image formation with sufficient accuracy.
The end goal of physical realism can facilitate the understanding of the physical universe, at the expense of costly computation.
Thus, computer graphics research has been mainly about how to hack this entire process to achieve maximum realism with minimum effort.
A key ingredient for this hack is the (limit of) human perception, where both sensorial and neural passageways cap the amount of generated information beyond which further enhancements cannot be perceived (and efforts wasted).
This “rendering hack” has long preceded computer graphics (or machine computation) in visual art, which are traditionally based on manual efforts and thus impractical for brute force computation.
The end goal of artistic ideal can facilitate the understanding of perceptual mechanisms which in turn provide opportunities for subjective variations (such as different artistic styles and movements).
This is one main reason why I have been practicing drawing, sketching, painting, animation, design, and other creative forms. It is more of an exercise to see than to draw. (Besides, it is fun and diversifies my daily routines.)
“If you can’t explain it simply, you don’t understand it well enough.” – A quote attributed to Albert Einstein.
PS
Recent trends in black-box machine learning further moves in the opposite direction, by making the visual computation even more expensive and opaque than traditional forward rendering in computer graphics.
I look forward to see if machine learning can shed light on the artistic and subjective aspects of image formation.
