Output-Sensitive Rendering of Detailed Animated Characters for Crowd Simulation

High-quality, detailed animated characters are often represented as textured polygonal meshes. The problem with this technique is the high cost that involves rendering and animating each one of these characters. This problem has become a major limiting factor in crowd simulation. Since we want to render a huge number of characters in real-time, the purpose of this thesis is therefore to study the current existing approaches in crowd rendering to derive a novel approach. The main limitations we have found when using impostors are (1) the big amount of memory needed to store them, which also has to be sent to the graphics card, (2) the lack of visual quality in close-up views, and (3) some visibility problems. As we wanted to overcome these limitations, and improve performance results, the found conclusions lead us to present a new representation for 3D animated characters using relief mapping, thus supporting an output-sensitive rendering. The basic idea of our approach is to encode each character through a small collection of textured boxes storing color and depth values. At runtime, each box is animated according to the rigid transformation of its associated bone in the animated skeleton. A fragment shader is used to recover the original geometry using an adapted version of relief mapping. Unlike competing output-sensitive approaches, our compact representation is able to recover high-frequency surface details and reproduces view-motion parallax e ects. Furthermore, the proposed approach ensures correct visibility among di erent animated parts, and it does not require us to prede ne the animation sequences nor to select a subset of discrete views. Finally, a user study demonstrates that our approach allows for a large number of simulated agents with negligible visual artifacts

Parallelized Egocentric Fields for Autonomous Navigation

In this paper, we propose a general framework for local path-planning and steering that can be easily extended to perform high-level behaviors. Our framework is based on the concept of affordances: the possible ways an agent can interact with its environment. Each agent perceives the environment through a set of vector and scalar fields that are represented in the agent’s local space. This egocentric property allows us to efficiently compute a local space-time plan and has better parallel scalability than a global fields approach. We then use these perception fields to compute a fitness measure for every possible action, defined as an affordance field. The action that has the optimal value in the affordance field is the agent’s steering decision. We propose an extension to a linear space-time prediction model for dynamic collision avoidance and present our parallelization results on multicore systems. We analyze and evaluate our framework using a comprehensive suite of test cases provided in SteerBench and demonstrate autonomous virtual pedestrians that perform steering and path planning in unknown environments along with the emergence of high-level responses to never seen before situations

Look me in the eyes: A survey of eye and gaze animation for virtual agents and artificial systems

International audienceA person's emotions and state of mind are apparent in their face and eyes. As a Latin proverb states: "The face is the portrait of the mind; the eyes, its informers.". This presents a huge challenge for computer graphics researchers in the generation of artificial entities that aim to replicate the movement and appearance of the human eye, which is so important in human-human interactions. This State of the Art Report provides an overview of the efforts made on tackling this challenging task. As with many topics in Computer Graphics, a cross-disciplinary approach is required to fully understand the workings of the eye in the transmission of information to the user. We discuss the movement of the eyeballs, eyelids, and the head from a physiological perspective and how these movements can be modelled, rendered and animated in computer graphics applications. Further, we present recent research from psychology and sociology that seeks to understand higher level behaviours, such as attention and eye-gaze, during the expression of emotion or during conversation, and how they are synthesised in Computer Graphics and Robotics

Multilinear motion synthesis with level-of-detail controls

Interactive animation systems often use a level-of-detail(LOD) control to reduce the computational cost by eliminatingunperceivable details of the scene. Most methodsemploy a multiresolutional representation of animationand geometrical data, and adaptively change the accuracylevel according to the importance of each character.Multilinear analysis provides the efficient representation ofmultidimensional and multimodal data, including humanmotion data, based on statistical data correlations. Thispaper proposes a LOD control method of motion synthesiswith a multilinear model. Our method first extracts asmall number of principal components of motion samplesby analyzing three-mode correlations among joints, time,and samples using high-order singular value decomposition.A new motion is synthesized by interpolatingthe reduced components using geostatistics, where theprediction accuracy of the resulting motion is controlledby adaptively decreasing the data dimensionality. Weintroduce a hybrid algorithm to optimize the reductionsize and computational time according to the distancefrom the camera while maintaining visual quality. Ourmethod provides a practical tool for creating an interactiveanimation of many characters while ensuring accurate andflexible controls at a modest level of computational cost

Output-Sensitive Rendering of Detailed Animated Characters for Crowd Simulation

High-quality, detailed animated characters are often represented as textured polygonal meshes. The problem with this technique is the high cost that involves rendering and animating each one of these characters. This problem has become a major limiting factor in crowd simulation. Since we want to render a huge number of characters in real-time, the purpose of this thesis is therefore to study the current existing approaches in crowd rendering to derive a novel approach. The main limitations we have found when using impostors are (1) the big amount of memory needed to store them, which also has to be sent to the graphics card, (2) the lack of visual quality in close-up views, and (3) some visibility problems. As we wanted to overcome these limitations, and improve performance results, the found conclusions lead us to present a new representation for 3D animated characters using relief mapping, thus supporting an output-sensitive rendering. The basic idea of our approach is to encode each character through a small collection of textured boxes storing color and depth values. At runtime, each box is animated according to the rigid transformation of its associated bone in the animated skeleton. A fragment shader is used to recover the original geometry using an adapted version of relief mapping. Unlike competing output-sensitive approaches, our compact representation is able to recover high-frequency surface details and reproduces view-motion parallax e ects. Furthermore, the proposed approach ensures correct visibility among di erent animated parts, and it does not require us to prede ne the animation sequences nor to select a subset of discrete views. Finally, a user study demonstrates that our approach allows for a large number of simulated agents with negligible visual artifacts

Assessing the perceived realism of agent crowd behaviour within virtual urban environments using psychophysics

Inhabited virtual environments feature in a growing number of graphical applications. Simulated crowds are employed for different purposes; ranging from evaluation of evacuation procedures to driving interactable elements in video games. For many applications, it is important that the displayed crowd behaviour is perceptually plausible to the intended viewers. Crowd behaviour is inherently in flux, often depending upon many different variables such as location, situation and crowd composition. Researchers have, for a long time, attempted to understand and reason about crowd behaviour, going back as far as famous psychologists such as Gustave Le Bon and Sigmund Freud who applied theories of mob psychology with varying results. Since then, various other methods have been tried, from articial intelligence to simple heuristics, for crowd simulation. Even though the research into methods for simulating crowds has a long history, evaluating such simulations has received less attention and, as this thesis will show, increased complexity and high-delity recreation of recorded behaviours does not guarantee improvement in the plausibility for a human observer. Actual crowd data is not always perceived more real than simulation, making it dicult to identify gold standards, or a ground truth. This thesis presents new work on the use of psychophysics for perceptual evaluation of crowd simulation in order to develop methods and metrics for tailoring crowd behaviour for target applications. Psychophysics itself is branch of psychology dedicated to studying the relationship between a given stimuli and how it is perceived. A three-stage methodology of analysis, synthesis and perception is employed in which crowd data is gathered from the analysis of real instances of crowd behaviour and then used to synthesise behavioural features for simulation before being perceptually evaluated using psychophysics. Perceptual thresholds are calculated based on the psychometric function and key congurations are identied that appear the most perceptually plausible to human viewers. The method is shown to be useful for the initial application and it is expected that it will be applicable to a wide range of simulation problems in which human perception and acceptance is the ultimate measure of success

VRpursuits: Interaction in Virtual Reality Using Smooth Pursuit Eye Movements

Gaze-based interaction using smooth pursuit eye movements (Pursuits) is attractive given that it is intuitive and overcomes the Midas touch problem. At the same time, eye tracking is becoming increasingly popular for VR applications. While Pursuits was shown to be effective in several interaction contexts, it was never explored in-depth for VR before. In a user study (N=26), we investigated how parameters that are specific to VR settings influence the performance of Pursuits. For example, we found that Pursuits is robust against different sizes of virtual 3D targets. However performance improves when the trajectory size (e.g., radius) is larger, particularly if the user is walking while interacting. While walking, selecting moving targets via Pursuits is generally feasible albeit less accurate than when stationary. Finally, we discuss the implications of these findings and the potential of smooth pursuits for interaction in VR by demonstrating two sample use cases: 1) gaze-based authentication in VR, and 2) a space meteors shooting game

Visual modeling and simulation of multiscale phenomena

Many large-scale systems seen in real life, such as human crowds, fluids, and granular materials, exhibit complicated motion at many different scales, from a characteristic global behavior to important small-scale detail. Such multiscale systems are computationally expensive for traditional simulation techniques to capture over the full range of scales. In this dissertation, I present novel techniques for scalable and efficient simulation of these large, complex phenomena for visual computing applications. These techniques are based on a new approach of representing a complex system by coupling together separate models for its large-scale and fine-scale dynamics. In fluid simulation, it remains a challenge to efficiently simulate fine local detail such as foam, ripples, and turbulence without compromising the accuracy of the large-scale flow. I present two techniques for this problem that combine physically-based numerical simulation for the global flow with efficient local models for detail. For surface features, I propose the use of texture synthesis, guided by the physical characteristics of the macroscopic flow. For turbulence in the fluid motion itself, I present a technique that tracks the transfer of energy from the mean flow to the turbulent fluctuations and synthesizes these fluctuations procedurally, allowing extremely efficient visual simulation of turbulent fluids. Another large class of problems which are not easily handled by traditional approaches is the simulation of very large aggregates of discrete entities, such as dense pedestrian crowds and granular materials. I present a technique for crowd simulation that couples a discrete per-agent model of individual navigation with a novel continuum formulation for the collective motion of pedestrians. This approach allows simulation of dense crowds of a hundred thousand agents at near-real-time rates on desktop computers. I also present a technique for simulating granular materials, which generalizes this model and introduces a novel computational scheme for friction. This method efficiently reproduces a wide range of granular behavior and allows two-way interaction with simulated solid bodies. In all of these cases, the proposed techniques are typically an order of magnitude faster than comparable existing methods. Through these applications to a diverse set of challenging simulation problems, I demonstrate the benefits of the proposed approach, showing that it is a powerful and versatile technique for the simulation of a broad range of large and complex systems