Title:
Director in a box: learning cinematic rhetoric for camera shot selection

dc.contributor.author Munro, John Burnet en_US
dc.contributor.department Computer Science en_US
dc.date.accessioned 2010-12-21T15:28:24Z
dc.date.available 2010-12-21T15:28:24Z
dc.date.issued 2010-12-20 en_US
dc.description.abstract Automatic generation of cinematic content has been a goal for both the military and the entertainment industry to allow more diverse plot structures so that a trainee or player may have a scenario tailored to their personal needs and desires. We approach this problem from a traditional view of story as being appropriately broken into two parts: plot and discourse. We focus on the rhetorical aspects of discourse, specifically selecting coherent and aesthetically pleasing shot and blocking constraints for a virtual cinematographer. In the past, selection has been solved using a decompositional planning approach. Unfortunately, each decompositional unit corresponding to a single film idiom must be hand-authored by an expert cinematographer, resulting in an intractable knowledge acquisition problem, prone to error and subjectivity. We show that this problem can instead be solved by reinforcement learning techniques, which train on features from existing sitcom and movie scenes. We will evaluate the precision of our method by running 10-fold cross validation on our training sets. en_US
dc.description.advisor Committee Member/Second Reader: Isbell, Charles; Faculty Mentor: Riedl, Mark en_US
dc.identifier.uri http://hdl.handle.net/1853/36558
dc.language.iso en_US en
dc.publisher Georgia Institute of Technology en_US
dc.subject Cinematography en_US
dc.subject Automation en_US
dc.subject Entertainment en_US
dc.subject Education en_US
dc.subject Computational narrative en_US
dc.title Director in a box: learning cinematic rhetoric for camera shot selection en_US
dc.type Text
dc.type.genre Undergraduate Thesis
dspace.entity.type Publication
local.contributor.corporatename College of Computing
local.contributor.corporatename School of Computer Science
local.contributor.corporatename Undergraduate Research Opportunities Program
local.relation.ispartofseries Undergraduate Research Option Theses
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relation.isOrgUnitOfPublication 0db885f5-939b-4de1-807b-f2ec73714200
relation.isSeriesOfPublication e1a827bd-cf25-4b83-ba24-70848b7036ac
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