Example-driven virtual cinematography by learning camera behaviors

Designing a camera motion controller that has the capacity to move a virtual camera automatically in relation with contents of a 3D animation, in a cinematographic and principled way, is a complex and challenging task. Many cinematographic rules exist, yet practice shows there are significant stylistic variations in how these can be applied. In this paper, we propose an example-driven camera controller which can extract camera behaviors from an example film clip and re-apply the extracted behaviors to a 3D animation, through learning from a collection of camera motions. Our first technical contribution is the design of a low-dimensional cinematic feature space that captures the essence of a film's cinematic characteristics (camera angle and distance, screen composition and character configurations) and which is coupled with a neural network to automatically extract these cinematic characteristics from real film clips. Our second technical contribution is the design of a cascaded deep-learning architecture trained to (i) recognize a variety of camera motion behaviors from the extracted cinematic features, and (ii) predict the future motion of a virtual camera given a character 3D animation. We propose to rely on a Mixture of Experts (MoE) gating+prediction mechanism to ensure that distinct camera behaviors can be learned while ensuring generalization. We demonstrate the features of our approach through experiments that highlight (i) the quality of our cinematic feature extractor (ii) the capacity to learn a range of behaviors through the gating mechanism, and (iii) the ability to generate a variety of camera motions by applying different behaviors extracted from film clips. Such an example-driven approach offers a high level of controllability which opens new possibilities toward a deeper understanding of cinematographic style and enhanced possibilities in exploiting real film data in virtual environments.