A Virtual Director Using Hidden Markov Models

dc.contributor.authorMerabti, B.en_US
dc.contributor.authorChristie, M.en_US
dc.contributor.authorBouatouch, K.en_US
dc.contributor.editorChen, Min and Zhang, Hao (Richard)en_US
dc.date.accessioned2016-12-08T11:25:34Z
dc.date.available2016-12-08T11:25:34Z
dc.date.issued2016
dc.description.abstractAutomatically computing a cinematographic consistent sequence of shots over a set of actions occurring in a 3D world is a complex task which requires not only the computation of appropriate shots (viewpoints) and appropriate transitions between shots (cuts), but the ability to encode and reproduce elements of cinematographic style. Models proposed in the literature, generally based on finite state machine or idiom‐based representations, provide limited functionalities to build sequences of shots. These approaches are not designed in mind to easily learn elements of cinematographic style, nor do they allow to perform significant variations in style over the same sequence of actions. In this paper, we propose a model for automated cinematography that can compute significant variations in terms of cinematographic style, with the ability to control the duration of shots and the possibility to add specific constraints to the desired sequence. The model is parametrized in a way that facilitates the application of learning techniques. By using a Hidden Markov Model representation of the editing process, we demonstrate the possibility of easily reproducing elements of style extracted from real movies. Results comparing our model with state‐of‐the‐art first‐order Markovian representations illustrate these features, and robustness of the learning technique is demonstrated through cross‐validation.Automatically computing a cinematographic consistent sequence of shots over a set of actions occurring in a 3D world is a complex task which requires not only the computation of appropriate shots (viewpoints) and appropriate transitions between shots (cuts), but the ability to encode and reproduce elements of cinematographic style. Models proposed in the literature, generally based on finite state machine or idiom‐based representations, provide limited functionalities to build sequences of shots. These approaches are not designed in mind to easily learn elements of cinematographic style, nor do they allow to perform significant variations in style over the same sequence of actions. In this paper, we propose a model for automated cinematography that can compute significant variations in terms of cinematographic style, with the ability to control the duration of shots and the possibility to add specific constraints to the desired sequence. The model is parametrized in a way that facilitates the application of learning techniques. By using a Hidden Markov Model representation of the editing process, we demonstrate the possibility of easily reproducing elements of style extracted from real movies. Results comparing our model with state‐of‐the‐art first‐order Markovian representations illustrate these features, and robustness of the learning technique is demonstrated through cross‐validation.en_US
dc.description.number8
dc.description.sectionheadersArticles
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume35
dc.identifier.doi10.1111/cgf.12775
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.12775
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf12775
dc.publisher© 2016 The Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectanimation systems
dc.subjectanimation
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Three‐Dimensional Graphics and Realism—Animation
dc.titleA Virtual Director Using Hidden Markov Modelsen_US
Files
Collections