Burelli, Paolo

Cinematographic games are a rising genre in the computer games industry and an increasing number of titles published include some aspects of cinematography in the gameplay or the storytelling. At present state, camera handling in computer games is managed primarily through custom scripts and animations and there is an inverse relationship between player freedom and cinematographic quality. In this paper, we present a description of a series of technical challenges connected with the development of an automatic camera control library for computer games and we showcase a set of algorithmic and engineering solutions

    Aalborg UniversitetImplementing Game CinematographyBurelli, PaoloPublished in:Eurographics Workshop on Intelligent Cinematography and EditingDOI (link to publication from Publisher):10.2312/wiced.20151080Publication date:2015Document VersionEarly version, also known as pre-printLink to publication from Aalborg UniversityCitation for published version (APA):Burelli, P. (2015). Implementing Game Cinematography: Technical Challenges and Solutions for AutomaticCamera Control in Games. In Eurographics Workshop on Intelligent Cinematography and Editing EuropeanAssociation for Computer Graphics. DOI: 10.2312/wiced.20151080General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            ? 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Ronfard (Editors)Implementing Game Cinematography:Technical Challenges and Solutionsfor Automatic Camera Control in GamesPaolo Burelli - Aalborg University Copenhagen, DenmarkAbstractCinematographic games are a rising genre in the computer games industry and an increasing numberof titles published include some aspects of cinematography in the gameplay or the storytelling. Atpresent state, camera handling in computer games is managed primarily through custom scripts andanimations and there is an inverse relationship between player freedom and cinematographic quality. Inthis paper, we present a description of a series of technical challenges connected with the developmentof an automatic camera control library for computer games and we showcase a set of algorithmic andengineering solutions.Categories and Subject Descriptors (according to ACM CCS): I.3.6 [Computer Graphics]: Methodol-ogy and Techniques—[Interaction techniques] I.2.8 [Artificial Intelligence]: Problem Solving, ControlMethods, and Search—[Heuristic methods]1. IntroductionCamera control in computer games is one of the keyaspects of player experience [Bur13]. The virtual cam-era represents the point of view of the player, this itdeeply affects its ability to interact and understandthe game narrative. Between standard manual andstatic camera placement, different control paradigmshave been developed in the industry, mostly to ac-commodate specific forms of gameplay [HH09]. Re-search in automatic camera control, instead, aims atdefining general methods to control efficiently an ef-fectively the camera behaviour. Through automaticcamera control, game designers and developers can de-fine high-level and environment-independent require-ments, such as the visibility of a particular object orthe size of that object on the screen. Based on these re-quirements the camera should be automatically placedand animated while reacting to changes in the virtualenvironment.Finding and tracking the optimal camera that max-imises the fulfilment of the designer requirements is acomplex optimisation problem; the complexity of theobjective function evaluation and the short executiontime imposed to ensure a real-time execution, makethe problem computationally hard. Moreover, in real-time automatic camera control, the algorithm needsto find and track the best possible camera configura-tion in an environment that changes continuously andin an unpredictable manner.On top of these purely algorithmic aspects, there area number of challenges related to the engineering of acamera control system for computer games. Address-ing issues, such as how to adapt the system to differenttypes of environments and geometries or how to en-sure smooth camera animations, is as important as de-signing an efficient camera optimisation algorithm. Inthis article, based on the design and implementationof an automatic camera control library for computergames named CamOn †, we describe the challengesaddressed and the solutions employed. Furthermore,we evaluate these solutions from a performance per-spective on benchmark for real-time automatic cameracontrol [BY15].2. BackgroundSince the introduction of 3D computer graphics, theautomation of virtual camera control represents as a† http://github.com/paoloburelli/camonc© The Eurographics Association 2015.Paolo Burelli / Implementing Game Cinematographychallenging task [CON08]. The task is commonly mod-elled as a constraint satisfaction problem, in which adesigner defines a set of desired frame properties thatare modelled as an objective function to be maximisedby a solver. These frame properties, often referred toas frame constraints, describe how the frame shouldlook like in terms of composition [BMBT00].Pure optimisation approaches, such as the SmartViewpoint Computation Library [RU14], implementa flexible search strategy that models all frame con-straints as an objective function (a weighted sum ofeach constraint) allowing for partial satisfaction of anyconstraint. The flexibility of such approaches comeswith the price of a high computational cost, which isa particularly critical factor in real-time dynamic vir-tual environments such as computer games. A numberof solutions [BSG08, BJ09] attempted to address thiscomputational employ by employing local search algo-rithms.Inspired by the work of Ranon and Urli [RU14] onimproving efficiency of static virtual camera optimisa-tion, this article builds upon these early works on dy-namic camera optimisation with local search [BJ09] byintroducing a series of technical and algorithmic im-provements targeted at increasing the quality of theshots produced.3. Automatic Camera Control in GamesThe problem of automatic camera control in inter-active environments can be described as a composi-tion of three interconnected subtasks: shot definitionand planning, virtual camera composition and cameraanimation. Shot definition and planning can be de-scribed as the task of defining a sequence of shots tovisualise one or more events in a virtual environment.Virtual camera composition is the process of translat-ing these shots in actual camera configurations, whilecamera animation is the process of animating the cam-era during the shots and ensuring smoothness betweenthem. In the process of translating this framework in acamera control library, we identified four components:shot, actor, solver and camera operator.Shot A shot, in cinematography, describes an unin-terrupted series of frames with common compositionproperties [Skl93]. We employ the shot concept todescribe the requirements for the camera behaviourin terms of how the frame generated by the camerashould look like. A shot is composed, therefore, by anumber of actors – not necessarily humans – and a de-scription of the composition properties of the frames.Based on the work by Burelli and Yannakakis [BY15]and Ranon et al. [RCU10], who performed a system-atic analysis of frame constraints for virtual camera(a) (b)Figure 1: Examples of shot (a) and actor (b) definitioncomposition, the latest version of CamOn supports thefollowing five properties:• Visibility• Projection Size• Vantage Angle• Relative Position• Position On ScreenA shot is defined as an environment independent en-tity that can be created, edited and loaded when nec-essary. Figure 1a shows an example of the definitionof a shot with the number of actors and a list of frameconstraints. Furthermore, for each actor, it is possibleto define a volume of interest within the actor. Thisis necessary to focus the shot on a part of the actorallowing, for instance, a close-up shot to focus on theface.Actor An actor is an entity representing a virtualworld object in a shot, its purpose is to give a standardrepresentation of different types of objects that can beused throughout the camera control flow to animatethe camera. An actor represents the connected objectwith a proxy geometry that can be adapted to the ob-ject’s shape and size (see Figure 1b). The necessitiesfor such an abstraction are manifold. First, the virtualobjects might contain animations that should not betaken into consideration by the camera; for instance,a 3D model oscillating during a walking cycle shouldnot, in most cases, transmit its oscillation to the cam-era. Likewise, the virtual object might be affected bytransformations that should not be considered by thecamera, such as the rotation of a rolling ball. More-over, contrarily to a bounding box or a boundingsphere, the actor’s representation is independent fromthe original geometry and can be adapted to the tar-get object’s shape without affecting the internal mech-anisms of the game engine – e.g physics. For instance,the representation of a butterfly might not include thewings and, in the same way, the representation of aheadless man might include the non-existing head.Camera Operator The camera operator componentis the central component connecting shot, actors andc© The Eurographics Association 2015.Paolo Burelli / Implementing Game Cinematographysolver. It instructs the solver with a specific shot andset of actors and, at every frame, it uses the solver’scalculated camera configuration to drive the cameraanimation. This component allows control of the cam-era dynamic behaviour and it ensures that the camerais animated smoothly guaranteeing frame coherence.This process is called camera motion damping and it isperformed by smoothing the changes of camera veloc-ity [HH09]. The motion damping in CamOn is imple-mented through a spring-damping method connectingthe current camera position with the ideal camera cal-culated by the solver.Solver The solver component is responsible for inter-preting the given shot, analysing the scene and findingthe camera configuration that best matches the de-sired composition properties. In computer games andinteractive applications the solver process is not in-tended as a finite optimisation that produces an op-timal set of results at the end of its execution. Itis, instead, a never-ending process that continuouslyadapts and tracks the best possible camera configura-tion while the environment is changing. This type ofproblem process is commonly defined as dynamic opti-misation problem (DOP), in which the objective func-tion of this problem is the linear combination of theobjective functions corresponding to the frame con-straints defined in the shot and the solution space con-tains all possible camera configurations.In this article we propose an extension of one ofthe author’s earlier works on dynamic camera controlwith local search [BJ09] based on the Artificial Poten-tial Field algorithm. In the process of engineering theCamOn library, we have analysed which aspects of thesolver can be improved based on the type of shot andthe current state of the environment. We identified thethree aspects: initialisation, movement compensationand weight management. These improvements are in-dependent of the type of solver and can be applied toany dynamic optimisation algorithm for virtual cam-era compositionThe improved initialisation is based on the resultspresented by Ranon and Urli [RU14], which suggesta strategy for particles initialisation to reduce theconvergence time of Particle Swarm Optimisation forstatic camera composition. To adapt this strategy toa local search algorithm we propose to prioritise theframe constraints and to calculate the initial positionbased on the first vantage angle or relative positionframe constraint. Furthermore, in order to void earlyconvergence based on complete occlusion, the initialposition should be moved close enough to the targetto guarantee the presence of no obstacles between thetwo.The second improvement, movement compensation,(a) Forest - one actor (b) House - one actor(c) House - three actors (d) Clearing - three actorsFigure 2: Screenshots taken showing the test scenarios.is based on Halper’s solution to enforce frame coher-ence and consists in interpolating the trajectory of theactors. In this context the interpolation is used to com-pensate the position of the last optimum so that thelocal search algorithm is able to explore areas of thespace that are more likely to contain an optimal solu-tion.The last improvement focuses on dynamically tun-ing the weights of the frame constraints in the ob-jective function. These weights are normally constantthroughout the whole optimisation process; however,this might provoke some undesired behaviours in thelocal search process. For instance, in the cases in whichone or more actors in the shot are completely non visi-ble, the frame constraints corresponding to those sub-jects are have still an impact on the objective function.This means that the solver could potentially select so-lutions in which no actor is visible, but the camera isplaced at the correct vantage angle for all actors.To avoid these situations, the weight of the frameconstraints referred to an actor can be scaled by afactor inversely proportional to the occlusion of thatactor, giving higher propriety to the frame constraintsreferring to visible actors. Furthermore, using a sec-ond or higher power of the occlusion can be used tosmoothly reduce the weights only at high levels of oc-clusion – e.g. when the actor is almost non visible.The same principle principle can be applied also to thefunction used to estimate the level of visibility of anactor. Using a power of the visibility – the current im-plementation uses a 4th power – instead of the originalestimation allows the solver to handle more effectivelysparse occlusion [CNO12]. Modifying in this mannerthe objective function induces the search algorithm todiscard solutions that offer little improvement in visi-bility at a high cost of the other objective function.c© The Eurographics Association 2015.Paolo Burelli / Implementing Game Cinematography4. Performance EvaluationTo assess the impact of the improvements proposedin this article, we conducted an experimental evalu-ation comparing the original version of the APF al-gorithm for virtual camera composition [BJ09] witha version including these improvements. The exper-iment was carried using the benchmark applicationpresented in [BY15] on an Intel i7 at 2.3 Ghz with8GBytes of RAM and with an NVIDIA GeForce GT650M graphics card running Mac OS X 10.10.Among the eleven scenarios presented in the afore-mentioned benchmark, we selected the subset of sce-narios that mimic more closely an in-game situation.The selected subset contains four scenarios in which aProjection Size, a Vantage Angle and a Visibility con-straints are imposed on one or three actors in differentenvironments as described in Figure 2. In each sce-nario, the two versions of the system have been com-pared on 20 seconds runs repeated for 20 times; theperformance measure employed is the average qual-ity of the frames produced, which is calculated usingthe average best function value commonly employedin dynamic optimisation [Sch07].Table 1 shows the average values of accuracy overthe 20 runs; the p-value in each scenario is the resultof a two tailed t-test between the two tested versions.In the forest scenario with one actor, a small but sig-nificant improvement is achieved; the good results inthis environment suggest that the improvements in-troduced have a positive impact in the system’s abil-ity to handle sparse occluders as this scenario is madeof trees, grass and wooden fences. While no significantimprovement is registered in scenario; in the third andfourth scenario, there is an increase of 23% and 45%in the accuracy. These scenarios include empty spacesdivided by large occluders that can potentially gen-erate situations in which only one or two actors canbe visible at the same time (see Figures 2c and 2d).The ability of the improved algorithm to dynamicallychange the weights of the non-visible actors helps thesolver in these situations to concentrate on the bestsolution candidates.5. ConclusionThe article presented a series of domain specific im-provements for automatic camera control in computergames. A performance evaluation was conducted, re-vealing how the proposed improvements applied to anexisting camera control system are able to increase thesystem’s performance. We believe that the study pre-sented can help practitioners and researchers to havea better understanding of the challenges of automaticcamera control. Furthermore, the library developed asScenario Improved Basic p-valuea 1.00 0.98 0.00b 0.99 0.98 0.12c 0.52 0.42 0.00d 0.58 0.40 0.00Table 1: Average shot quality in each scenario.a result of this research study can serve as a startingpoint for future experiment about game cinematogra-phy.References[BJ09] Burelli P., Jhala A.: Dynamic Artificial Po-tential Fields for Autonomous Camera Control. InAAAI Conference On Artificial Intelligence In Inter-active Digitale Entertainment Conference (Palo Alto,2009), AAAI.[BMBT00] Bares W. H., McDermott S., BoudreauxC., Thainimit S.: Virtual 3D camera composition fromframe constraints. In ACM Multimedia (Marina del Rey,California, USA, 2000), ACM Press, pp. 177–186.[BSG08] Bourne O., Sattar A., Goodwin S.: AConstraint-Based Autonomous 3D Camera System.Journal of Constraints 13, 1-2 (2008), 180–205.[Bur13] Burelli P.: Virtual Cinematography in Games: Investigating the Impact on Player Experience. In In-ternational Conference On The Foundations Of DigitalGames (Chania, Greece, 2013), Society for the Advance-ment of the Science of Digital Games, pp. 134–141.[BY15] Burelli P., Yannakakis G. N.: A BenchmarkFor Virtual Camera Control in Computer Games. InApplications of Evolutionary Computation (2015).[CNO12] Christie M., Normand J.-M., Olivier P.:Occlusion-free camera control for multiple targets. InACM SIGGRAPH/Eurographics Symposium on Com-puter Animation (July 2012), Eurographics Association,pp. 59–64.[CON08] Christie M., Olivier P., Normand J. M.:Camera control in computer graphics. In ComputerGraphics Forum (2008), vol. 27, pp. 2197–2218.[HH09] Haigh-Hutchinson M.: Real-Time Cameras.Elsevier, 2009.[RCU10] Ranon R., Christie M., Urli T.: AccuratelyMeasuring the Satisfaction of Visual Properties in Vir-tual Camera Control. In International symposium onSmart Graphics (Banff, Canada, 2010), Springer-Verlag,pp. 91–102.[RU14] Ranon R., Urli T.: Improving the Efficiency ofViewpoint Composition. IEEE Transactions on Visual-ization and Computer Graphics 2626, c (2014), 1–1.[Sch07] Scho¨nemann L.: Evolution Strategies in Dy-namic Environments. In Evolutionary Computation inDynamic and Uncertain Environments, Yang S., OngY.-S., Jin Y., (Eds.), vol. 51 of Studies in Computa-tional Intelligence. Springer Berlin Heidelberg, Berlin,Heidelberg, 2007, pp. 51–77.[Skl93] Sklar R.: Film: An International History of theMedium. Thames and Hudson, 1993.c© The Eurographics Association 2015.

Implementing Game Cinematography:Technical Challenges and Solutions for Automatic Camera Control in Games

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Implementing Game Cinematography:Technical Challenges and Solutions for Automatic Camera Control in Games

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