Abstract
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.
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.