Creto Augusto Vidal

Using natural vibrations to guide control for locomotion

Control for physically based characters presents a challenging task because it requires not only the management of the functional aspects that lead to the successful completion of the desired task, but also the resulting movement must be visually appealing and meet the quality requirements of the application. Crafting controllers to generate desirable behaviors is difficult because the specification of the final outcome is indirect and often at odds with the functional control of the task. This paper presents a method which exploits the natural modal vibrations of a physically based character in order to provide a palette of basis coordinations that animators can use to assemble their desired motion. A visual user interface allows an animator to guide the final outcome by selecting and inhibiting the use of specific modes. Then, an optimization routine applies the user-chosen modes in the tuning of parameters for a fixed locomotion control structure. The result is an animation system that is easy for an animator to drive and is able to produce a wide variety of locomotion styles for varying character morphologies.

A distributed-memory parallel technique for two-dimensional mesh generation for arbitrary domains

This work describes a technique for generating two-dimensional triangular meshes using distributed memory parallel computers, based on a master/slaves model. This technique uses a coarse quadtree to decompose the domain and a serial advancing front technique to generate the mesh in each subdomain concurrently. In order to advance the front to a neighboring subdomain, each subdomain suffers a shift to a Cartesian direction, and the same advancing front approach is performed on the shifted subdomain. This shift-and-remesh procedure is repeatedly applied until no more mesh can be generated, shifting the subdomains to different directions each turn. A finer quadtree is also employed in this work to help estimate the processing load associated with each subdomain. This load estimation technique produces results that accurately represent the number of elements to be generated in each subdomain, leading to proper runtime prediction and to a well-balanced algorithm. The meshes generated with the parallel technique have the same quality as those generated serially, within acceptable limits. Although the presented approach is two-dimensional, the idea can be easily extended to three dimensions.

Simulation of genetic inheritance in the generation of virtual characters

Nowadays, applications of virtual reality (VR) and computer games use human characters models with ever-increasing sophistication. Additional challenges are posed by applications, such as life-simulation computer games (The Sims, Spore, etc.), internet-based virtual worlds (Second Life) and animation movies, that require simulation of kinship and interaction between isolated populations with well defined ethnic characteristics. The main difficulty in those situations is to generate models automatically, which are physically similar to a given population or family. In this paper, human reproduction is mimicked to produce character models, which inherit genetic characteristics from their ancestors. Unlike morphing techniques, in our method, it is possible that a genetic characteristic from an ancestor be manifested only after a few generations.

A biologically inspired generation of virtual characters

A number of techniques for generating geometric models of human head and body are in use nowadays. Models of human characters are useful in computer games, virtual reality, and many other applications. The complexities involved in generating such models, however, impose heavy limitations on the variety of characters produced. In this paper, diploid reproduction is mimicked to produce an unlimited number of character models, which inherit traits from two parent models. The meshes of all models are constructed based on control parameters that are distributed as genes among a group of chromosomes. Thus, the technique consists of distributing pre-selected characteristics, represented as control parameters, over a pre-determined number of chromosome pairs for both parents; followed by a simulated generation of the fathers and the mothers gametes; which are randomly combined in a simulated fecundation. The diversity is ensured in four random processes: the random exchange of segments during crossover; the random alignment of homologous chromosomes at metaphases I and II of meiosis; and the random union of male and female gametes during fecundation.

Virtual Reality in Brazil 2011: Reproducing virtual characters

Many virtual reality applications and games need a large number of virtual characters. Some of these applications require, in addition to the quantity, the simulation of kinship and evolution, not only of human character models, but also of different types of animals, toon models or other creatures. Some applications also require interactions between isolated populations with well-defined ethnic characteristics. The identification of similar traits between individuals of the same family is crucial to providing increased realism to many of these systems. The main difficulty in these situations is to generate models automatically, in real time, which are physically similar to a given population or family. In this paper, we extended our previous work, in which the reproduction of diploid beings is mimicked to produce character models that inherit genetic characteristics from their ancestors. Unlike morphing techniques, in our method, it is possible that a genetic characteristic from an ancestor be manifested only after a few generations.

Evolving Plastic Neuromodulated Networks for Behavior Emergence of Autonomous Virtual Characters

This paper addresses the problem of generating natural behavior of autonomous virtual characters. Inspired by the fields of Embodied and Enactive Artificial Intelligence, we postulate that natural behavior is the result of a coupling between the agent and the world where it lives, which leads to a coherence between its actions and its surroundings. In this work, we present the tools that we have been using to study that idea: a controller based on a plastic neuromodulated neural network, which is capable of molding itself to received stimuli; and a simple novel method for genetic encoding of artificial neural networks. We show the capabilities of the controller in generating interesting foraging behavior of an autonomous virtual robot, and discuss the advantages of its emergent characteristics when compared with traditional approaches.

Three-Dimensional Face Caricaturing by Anthropometric Distortions

Caricatures are models of persons or things in which certain striking characteristics are exaggerated in order to create a comic or grotesque effect. This paper is concerned with a strategy for automatically generating caricatures of threedimensional models based on anthropometric measures and geometric manipulations by influence zones. In the proposed strategy, measures from a reference model serve as means of comparison with the corresponding measures in the model to be caricatured. Deformations are applied to the features that differ most from the corresponding features in the reference model. This method is independent of mesh topology. Unlike other techniques, it is possible to generate variations of caricatures, adopting different sequences of deformations, application of asymmetry and expressions. Our method also does not need 3D model databases to be used as a base of combinations or comparisons of models.

System Model for Shooting Training Based on Interactive Video, Three-Dimensional Computer Graphics and Laser Ray Capture

Computer simulation based interactive systems have proven to be risk-free alternatives for training the police force. Also, in these kinds of simulations, the user can make errors without dangerous consequences, unlike real-world situations. In this sense, this paper proposes a system model, aimed at shooting training, using features of video sequences and three-dimensional computer graphics interactive environments. This proposal is initially based on the evaluation of a commercial system that uses immersive virtual reality. Then, it was defined a system model, based on which, two prototypes were developed. These prototypes are already in use in a police academy, and make use of audiovisual devices and laser shots emission.

Simple Feedforward Control for Responsive Motion Capture-Driven Simulations

Combining physically based simulation and motion capture data for animation is becoming a popular alternative to large motion databases for rich character motion. In this paper, our focus is on adapting motion-captured sequences for character response to external perturbations. Our technique is similar to approaches presented in the literature, but we propose a novel, straightforward way of computing feedforward control. While alternatives such as inverse dynamics and feedback error learning (FEL) exist, they are more complicated and require offline processing in contrast to our method which uses an auxiliary dynamic simulation to compute feedforward torques. Our method is simple, general, efficient, and can be performed at runtime. These claims are demonstrated through various experimental results of simulated impacts.

Gradient-based steering for vision-based crowd simulation algorithms

Most recent crowd simulation algorithms equip agents with a synthetic vision component for steering. They offer promising perspectives through a more realistic simulation of the way humans navigate according to their perception of the surrounding environment. In this paper, we propose a new perception/motion loop to steering agents along collision free trajectories that significantly improves the quality of vision‐based crowd simulators. In contrast with solutions where agents avoid collisions in a purely reactive (binary) way, we suggest exploring the full range of possible adaptations and retaining the locally optimal one. To this end, we introduce a cost function, based on perceptual variables, which estimates an agents situation considering both the risks of future collision and a desired destination. We then compute the partial derivatives of that function with respect to all possible motion adaptations. The agent then adapts its motion by following the gradient. This paper has thus two main contributions: the definition of a general purpose control scheme for steering synthetic vision‐based agents; and the proposition of cost functions for evaluating the perceived danger of the current situation. We demonstrate improvements in several cases.