Ambient video art is designed in the spirit of Brian Eno's ambient music - it must never require our attention, but must reward our attention whenever it is bestowed. It comes in many forms, ranging from the kitsch of the Christmas yule log broadcast to more mature moving image art created by a number of contemporary video artists and producers. The author has created a series of award-winning ambient video works. These works are designed to meet Eno's difficult requirements for ambient media - to never require but to always reward viewer attention in any moment. They are also intended to support viewer pleasure over a reasonable amount of repeated play. These works are all "linear" videos - relying on the careful sequencing and meticulous transitioning of images to reach their aesthetic goals. Re:Cycle uses a different approach. It relies on a computationally generative system to select and present shots in an ongoing flow - but with constant variations in both shot sequencing and transition choice. The Re:Cycle system runs indefinitely and avoids any significant repetition of shots and transitions. The system selects shots at random from a database of video clips, and joins them with transitions drawn at random from a separate transitions database. The transitions are based on abstract graphic values, so each specific visual transformation is unpredictable and complex. Compared with the linear videos, the computational system has sacrificed a measure of authorial control in order to maximize sequencing variability and therefore long term re-playability. The presentation describes in detail a series of specific artistic decisions made by the author and his production team. Each of these aesthetic design decisions is explicated as a balance between two fundamental variables: aesthetic control and system variability. The advantages and trade-offs of each decision point are identified and discussed. These artistic directions are analyzed in the broader context of generative art. This context situates the project within the discourse of generative art, and in the specifics of generative works in a variety of media, including visual art, sound art, moving image and literary works. The presentation also describes how metadata encoded within the shots and the transitions will be used to modulate the essentially random operation of the basic system in order to increase visual impact and flow. Future work on the system will incorporate this use of metadata - tagged as form and content variables for each shot, and as form variables for each transition. These metadata tags will provide increased coherence and continuity to the visual flow of the work. They will nuance and modify - but not completely supplant - the random processes at the heart of the generative system. The presentation concludes by describing how the system will be further revised to present emergent forms of generative narrative. It details how these storyworks could run indefinitely while mediating a dynamic balance between two seeming oppositions: random algorithmic selection and the coherence of sequencing necessary for narrative pleasure. (Source: Author's abstract, 2012 ELO Conference site)
background
Re:Cycle is a generative ambient video art piece based on nature imagery captured in the Canadian Rocky Mountains. Ambient video is designed to play in the background of our lives. It is a moving image form that is consistent with the ubiquitous distribution of ever-larger video screens. The visual aesthetic supports a viewing stance alternative to mainstream media - one that is quieter and more contemplative - an aesthetic of calmness rather than enforced immersion. An ambient video work is therefore difficult to create - it can never require our attention, but must always rewards viewer attention when offered. A central aesthetic challenge for this form is that it must also support repeated viewing. Re:Cycle relies on a generative recombinant strategy for ongoing variability, and therefore a higher measure of re-playability. It does so through the use of two random-access databases: one database of video clips, and another of video transition effects. The piece will run indefinitely, joining clips and transitions from the two databases in randomly varied combinations.
Creative input to the system derives in large part with the selection of shots that the artist uses. I've been fortunate to collaborate with a brilliant cinematographer - Glen Crawford from Canmore, Alberta. Re:Cycle's landscape images include a range of elements such as snow, trees, ice, clouds and water - reflecting a deep respect for the natural environment. These images also produce the ‘ambient’ quality I am seeking. They are engaging when viewed directly, but also move easily to the background when not. Another artist might choose very different images, and the resulting work could be completely different. While I enjoy the complete control offered with traditional linear video art, I am intrigued by the different set of artistic decisions this simple generative platform can support.
The current version of the generative engine for Re:Cycle also incorporates a deeper level of artistic intervention through the integration of metadata into the dynamics of the system. Each video clip is given one or more metadata tags - reflecting the content of the individual shot. I have used the tags to nuance the random operation of the engine, and group and present images in sequences that share a common content element (such as "snow" or "water"). The resulting generative video work presents a stronger sense of visual flow, and the sequencing begins to exhibit a degree of semantic continuity.
The overall design of the piece incorporates a series of decisions (number of shots, quality of shots, transition selection, algorithmic process) that strike a balance between replayability/variation on the one hand, and aesthetic control on the other.
Original program in MaxMSP-Jitter. Revised program in Max6
Director of Photography: Glen Crawford
Version 2 Programming: Sayeedeh Bayatpour, Tom Calvert
Original Programming: Wakiko Suzuki, Brian Quan, Majid Bagheri
Producer: Justine Bizzocchi