The rise of Unmanned Aerial Vehicles and their increasing use in the cinema industry calls for the creation of dedicated tools. Though there is a range of techniques to automatically control drones for a variety of applications, none have considered the problem of producing cinematographic camera motion in real-time for shooting purposes. In this paper we present our approach to UAV navigation for autonomous cinematography. The contributions of this research are twofold: (i) we adapt virtual camera control techniques to UAV navigation; (ii) we introduce a drone-independent platform for high-level user interactions that integrates cinematographic knowledge. The results presented in this paper demonstrate the capacities of our tool to capture live movie scenes involving one or two moving actors.
@cite_30: When creating real or computer graphics movies, the questions of how to layout elements on the screen, together with how to move the cameras in the scene are crucial to properly conveying the events composing a narrative. Though there is a range of techniques to automatically compute camera paths in virtual environments, none have seriously considered the problem of generating realistic camera motions even for simple scenes. Among possible cinematographic devices, real cinematographers often rely on camera rails to create smooth camera motions which viewers are familiar with. Following this practice, in this paper we propose a method for generating virtual camera rails and computing smooth camera motions on these rails. Our technique analyzes characters motion and user-defined framing properties to compute rough camera motions which are further refined using constrained-optimization techniques. Comparisons with recent techniques demonstrate the benefits of our approach and opens interesting perspectives in terms of creative support tools for animators and cinematographers.
@cite_22: This paper presents a system that generates cinematic replays for dialogue-based 3D video games. The system exploits the narrative and geometric information present in these games and automatically computes camera framings and edits to build a coherent cinematic replay of the gaming session. We propose a novel importance-driven approach to cinematic replay. Rather than relying on actions performed by characters to drive the cinematography (as in idiom-based approaches), we rely on the importance of characters in the narrative. We first devise a mechanism to compute the varying importance of the characters. We then map importances of characters with different camera specifications, and propose a novel technique that (i) automatically computes camera positions satisfying given specifications, and (ii) provides smooth camera motions when transitioning between different specifications. We demonstrate the features of our system by implementing three camera behaviors (one for master shots, one for shots on the player character, and one for reverse shots). We present results obtained by interfacing our system with a full-fledged serious game (Nothing for Dinner) containing several hours of 3D animated content.
@cite_24: Automatically positioning a virtual camera in a 3D environment given the specification of visual properties to be satisfied (on-screen layout of subjects, vantage angles, visibility) is a complex and challenging problem. Most approaches tackle the problem by expressing visual properties as constraints or functions to optimize, and rely on computationally expensive search techniques to explore the solution space. We show here how to express and solve the exact on-screen positioning of two or three subjects using a simple and very efficient technique. We express the solution space for each couple of subjects as a 2D manifold surface. We demonstrate how to use this manifold surface to solve Blinn's spacecraft problem with a straightforward algebraic approach. We extend the solution to three subjects and we show how to cast the complex 6D optimization problem tackled by most contributions in the field in a simple 2D optimization on the manifold surface by pruning large portions of the search space. The result is a robust and very efficient technique which finds a wide range of applications in virtual camera control and more generally in computer graphics.
@cite_15: 
@cite_17: The automated computation of appropriate viewpoints in complex 3D scenes is a key problem in a number of computer graphics applications. In particular, crowd simulations create visually complex environments with many simultaneous events for which the computation of relevant viewpoints remains an open issue. In this paper, we propose a system which enables the conveyance of events occurring in complex crowd simulations. The system relies on Reynolds' model of steering behaviors to control and locally coordinate a collection of camera agents similar to a group of reporters. In our approach, camera agents are either in a scouting mode, searching for relevant events to convey, or in a tracking mode following one or more unfolding events. The key benefit, in addition to the simplicity of the steering rules, holds in the capacity of the system to adapt to the evolving complexity of crowd simulations by self-organizing the camera agents to track interesting events.
Path planning has been challenging the research community for decades and the amount of literature on the matter is significant. In the robotic field especially, a number of approaches have addressed the problem. However, a large amount of this research was dedicated to ground vehicles and therefore did not fully exploit the capacities of UAV. Looking at the research conducted on path planning by the computer graphics community, the specific task of virtual camera control happens to be strongly related to our research topic due to the similar properties of drones and virtual cameras. In @cite_15 , Christie review a large spectrum of the literature on intelligent camera control, mainly consisting of optimization-based or constraint-based approaches. More recently, in @cite_24 , Lino proposed an algebraic solution to the problem of placing a camera given a set of visual properties. This seminal work on static camera placement was later used to propose camera path planning solutions. In Lino2015 and @cite_30 , the authors detail offline solutions to the problem. Closer to our problem, @cite_17 @cite_22 detailed a reactive approach based on steering behaviors.