1,720,979 research outputs found
Perception of Complex Emotional Body Language of a Virtual Character
No full textVirtual characters are a common phenomenon in serious game applications, and can enrich training environments for a range of different purposes. These characters can be used in games that have been developed to help people with learning difficulties. They can also be used to help users develop social skills, such as communication. For social interactions, much communicative information is contained in the body language between the parties involved. We know that humans are sensitive to emotions when they are conveyed on a virtual character and are capable of correctly identifying certain emotions. However, research on emotions and virtual characters tends to focus on a small number of emotions. We wish to create characters for a serious game who will convey a wide range of complex and subtle emotions. This paper presents a first investigation into the use of complex emotional body language for a virtual character. In two experiments, we examine participants’ perception of a range of motion-captured subtle emotions. Results from a pilot shows that participants are better able to recognise complex emotions with negative connotations rather than positive from a virtual character’s body motion. A second experiment aims to identify perceptual overlaps in these emotions, and results obtained motivate further investigation
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Multi-Modal Planning for Humanlike Motion Synthesis using Motion Capture
Full text linkPlanning the motions of a virtual character with high quality and control is a difficult challenge. Striking a balance between these two competing properties makes the problem particularly complex. While data-driven approaches produce high quality results due to the inherent realism of human motion capture data, planning algorithms are able to solve general continuous problems with a high degree of control. This dissertation addresses this overall problem with new techniques that combine the two approaches.Three main contributions are proposed. First, a simple and efficient motion capture segmentation mechanism is proposed based on geometric features that introduces semantic information for organizing a motion capture database into a motion graph. The obtained feature-based motion graph has less nodes and increased connectivity, which leads to improved searches in speed and coverage when compared to the standard approach. In addition, feature-based motion graphs enable a novel inverse branch kinematic deformation technique to be executed efficiently, allowing solution branches to be deformed towards precise goals without degrading the quality of the results.Second, in order to address speed of computation, precomputed motion maps are introduced for the interactive search and synthesis of locomotion sequences from unstructured feature-based motion graphs. Unstructured graphs can be successfully handled by relying on multiple maps and a search mechanism with backtracking information, which eliminates the need of manually creating fully connected move graphs. Precomputed motion maps can simultaneously search and execute motions in environments with many obstacles at interactive rates.Finally, a multi-modal data-driven framework is proposed for task-oriented human-like motion planning, which combines data-driven methods with parameterized motion skills in order to achieve human motions that are realistic and that have a high degree of controllability. The multi-modal planner relies on feature-based motion graphs for achieving a high-quality locomotion skill and integrates generic, task-specific data-based or algorithmic motion primitive skills for precise upper-body manipulation and action planning. The approach includes a multi-modal search method where primitive motion skills compete for contributing to the final solution.As a result, the overall proposed framework provides a high degree of control and, at the same time, retains the realism and human-likeness of motion capture data. Several examples are presented for synthesizing complex motions such as walking through doors, relocating books on shelves, etc
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Immersive Virtual Human Training Systems based on Direct Demonstration
Full text linkVirtual humans have great potential to become as effective as human trainers in monitored, feedback-based, virtual environments for training and learning. Thanks to recent advances on motion capture devices and stereoscopic consumer displays, animated virtual characters can now realistically interact with users in a variety of applications. Interactive virtual humans are in particular suitable for training systems where human-oriented motion skills or human-conveyed information are key to the learning material. This dissertation addresses the challenge of designing such training systems with the approach of motion modeling by direct demonstration and relying on immersive motion capture interfaces. In this way, experts in a training subject can directly demonstrate the needed motions in an intuitive way, until achieving the desired results.An immersive full-scale motion modeling interface is proposed for enabling users to model generic parameterized actions by direct demonstration. The proposed interface is based on aligned clusters of example motions, which can be interactively built until coverage of the target environment. After demonstrating the needed motions, the virtual trainer is then able to synthesize motions that are similar to the provided examples and at the same time are parameterized to generic targets and constraints. Hence, autonomous virtual trainers can subsequently reproduce the motions in generic training environments with apprentice users learning the training subject. The presented systems were implemented in a new development middleware that is scalable to different hardware configurations, from low-cost solutions to multi-tile displays, and it is designed to support distributed collaborative immersive virtual environments with streamed full-body avatar interactions. An immersive full-scale motion modeling interface is proposed for enabling users to model generic parameterized actions by direct demonstration. The proposed interface is based on aligned clusters of example motions, which can be interactively built until coverage of the target environment. After demonstrating the needed motions, the virtual trainer is then able to synthesize motions that are similar to the provided examples and at the same time are parameterized to generic targets and constraints. Hence, autonomous virtual trainers can subsequently reproduce the motions in generic training environments with apprentice users learning the training subject. The presented systems were implemented in a new development middleware that is scalable to different hardware configurations, from low-cost solutions to multi-tile displays, and it is designed to support distributed collaborative immersive virtual environments with streamed full-body avatar interactions. Given the several possible configurations for the proposed systems, this dissertation also analyzes the effectiveness of virtual trainers with respect to different choices on display size, use of avatars, and use of user-perspective stereo vision. Several experiments were performed to collect motion data during task performance under different configurations. These experiments expose and quantify the benefits of using stereo vision and avatars in motion reproduction tasks and show that the use of avatars improves the quality of produced motions. In addition, the use of avatars produced increased attention to the avatar space, allowing users to better observe and address motion constraints and qualities with respect to virtual environments. However, direct interaction in user-perspective leads to tasks executed in less time and to targets more accurately reached. These and other trade-offs were quantified and performed in conditions not investigated before.Finally, the proposed concepts were applied for the practical development of tools for delivering monitored upper-body physical therapy. New methods for exercise modeling, parameterization, and adaptation are presented in order to allow therapists to intuitively create, edit and re-use customized exercise programs that are responsive and adaptive to the needs of their patients. The proposed solutions were evaluated by therapists and demonstrate the suitability of the approach
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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Controlling The Physics of Humanoids
Full text linkHumanoid motion generation based on physics based controllers has the potential to revolutionize the realism and autonomy of motion in games, movies and even robotics. The area of simulation and control is broad and this thesis focuses on the problem of designing humanoid animations with physics-based controllers. Physics based animation has been an active field of research since the 90's but to this day has had rather minor appearances in the CG Industry. The primary setback is the complexity of the programming task that is needed and the lack of tools for artists to design robust controllers. The primary contribution of my work is the development of a set of tools that allow non-programmers to develop feedback based controllers to generate and parametrize different motion skills for a physics based character. In my system a user, who is knowledgeable about humanoid physics, is able to develop controllers for physics based characters with an intuitive user interface by visually creating a graph structure, independent of the character's mass properties, that represents the control hierarchy. Then our system explores the range of functionality of the input graph to make a parametrized controller. Our system has several low level components that maintain balance and allow the manipulation of the character configuration through Inverse Kinematics and Virtual Forces. I also present a control interface and network communication system to control motions for a humanoid robot and a method for using an XBox Kinect to recognize hand configurations. The intent of this last topic is to explore novel possible user interfaces to control and experiment with variations of physics controllers
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Toward real-time realistic humanoid manipulation tasks in changing environments
No full textA central challenging problem in humanoid robotics is to plan and execute dynamic tasks
in changing environments, and at the same time keep the result convincing and realistic.
Sampling-based online motion planners are particularly powerful for automatically
generating collision-free motions in changing environments. However, without learning
strategies, each task still has to be planned from scratch, preventing these algorithms from
getting closer to realtime performance. Moreover, the nature of the random sampling
strategy employed in these planners also results in extremely non human-like solutions.
This document addresses these two issues by proposing to learn important features from
previously planned solutions, or from real captured motion to improve both the efficiency
and the solution quality. Our methods work in changing environments, where obstacles
can have different positions in different tasks. However, we assume that obstacles are
static during the execution of a single task. We first propose the Attractor Guided Planner
(AGP), which extends existing motion planners in two simple but important ways. First,
it extracts significant attractor points from successful paths as guiding landmarks for new
similar tasks. Second, it relies on a task comparison metric to decide when previous
solutions should be reused to guide the planning of new tasks. The task comparison
metric takes into account the task specification and as well environment features which
are relevant to the query.
With combination of motion capture technique, the AGP planner also shows big improvements
towards generating realistic planned motions. We propose a constraint detection
method that applies to humanoid manipulation tasks. After recording a performer's
demonstrated motion, our method will automatically detect important constraints, and
then segment the input motion according to different types of constraints. Attractors are
placed at the connections between each pair of segments and assigned the same constraints
as the previous segment. Then, given a new similar task, the new planning is
guided not only toward the locations of the attractors, but also preserving the constraints
of the attractors.
Several experiments are presented with different humanoid reaching examples where
obstacles are differently located for each task. Our results show that the AGP greatly
improves both the planning time and solution quality, when comparing to traditional
sampling-based motion planners. We also show that with our constraint detection method,
the AGP planner can efficiently find a solution that preserves the features of the input motion,
making the solution motion coherent with the task being solved and therefore more
realistic. Although our current results are not yet capable of achieving real-time performance
nor overall realistic humanlike motions, we believe that the techniques introduced
here are key for getting closer to these goals
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Data-Based Motion Planning for Full-Body Virtual Human Interaction with the Environment
Full text linkAutonomous virtual characters are important in a growing number of applications ranging from simulation-based training to computer games. In this dissertation I propose a new data-based mobile manipulation framework for achieving real-time autonomous characters able to perform full-body interactions with the environment. The overall approach relies on few example motions in order to guide the generation of complex movements that replicate human-like characteristics.The proposed framework is based on three major components. The first one consists of a fast locomotion module that generates controllable models from single motion examples, and is capable of independent control of direction, orientation and velocity, with known coverage and quality characteristics. The approach is computationally efficient and achieves high controllability of stepping behaviors, thus addressing key properties for supporting a variety of whole-body manipulation tasks.The second component relies on the proposed locomotion controller applied to multiple locomotion behaviors in order to plan multi-behavior paths around obstacles. A new locomotion planning approach is proposed where the behavioral capabilities of the character are considered during the path planning stage, in order to address trade-offs related to path length and preferred navigation behavior when selecting narrow passages to take. The approach relies on new types of operations with planar navigation meshes, reaching fast execution times suitable for real-time applications.The last component focuses on the coordination between locomotion and upper-body manipulation. The proposed approach is based on learning spatial coordination features from example motions and on associating body-environment proximity information to the body configurations of each example motion. Body configurations then become the input to a regression system which in turn is able to generate new interactions for different situations in similar environments. The regression model is capable of encoding and replicating key spatial strategies with respect to body coordination and management of environment constraints. Obtained results successfully synthesize complex full-body actions such as opening doors and drawing in a wide whiteboard.The models proposed in this dissertation achieve new interactive controllers able to synthesize coordinated full-body motions for a variety of complex interactions requiring body mobility and manipulation
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GPU Rasterization Methods for Path Planning and Multi-Agent Navigation
Full text linkIn this dissertation I present new GPU-based approaches for addressing path planning and multi-agent navigation problems. The proposed methods rely on GPU rasterization techniques to construct navigation structures which allow us to address these problems in novel ways.There are three main contributions described in this document.The first is a new method for computing Shortest Path Maps (SPMs) for generic 2D polygonal environments. By making use of GPU shaders an approach is presented to implement the continuous Dijkstra’s wavefront propagation method, resulting in an SPM representation in a GPU’s buffer which can efficiently give a globally optimal shortest path between any point in the environment and the considered source point. The proposed shader-based approach also allows several extensions to be incorporated: multiple source points, multiple source segments, and the incorporation of weights that can alter the wavefront propagation in order to model velocity changes at vertices. These extensions allow SPMs to address a large range of real-world situations.The second contribution addresses the global coordination of multiple agents flowing from source to sink edges in a polygonal environment. The same GPU-based SPM methods are extended to compute a Continuous Max Flow in the input environment, which can be used to guide agents through the environment from source edges to sink edges, leading to a flow representation stored in the frame buffer of the GPU. A method for extracting flow lanes respecting clearance constraints is also presented, achieving the maximum possible number of lanes to route agents across an environment without ever creating bottlenecks.In order to address decentralized autonomous agents, the third contribution presents a new method for dynamically detecting and representing in SPMs regions where agents are bottlenecked. The incorporation of weighted barriers are proposed to model the corresponding time delays in corridors of the SPMs, in order to provide agents with alternative paths avoiding bottlenecks. In this way, a novel type of SPM is defined, providing optimal solutions from weights which reflect dynamic delays in the corridors of the environments.The methods proposed in this dissertation present novel approaches for addressing optimal paths and agent distribution in planar environments. Given the continuous development of high-performance GPUs, the proposed methods have the potential to open new avenues for the development of efficient navigation algorithms and representations
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Building Custom Real-Time Sensors for Virtual Reality Applications
Full text linkIn virtual reality (VR), real-time motion tracking is essential for the synchronization between virtual scenes and the real world. However, due to the limited availability of specific devices and the often expensive cost of existing tracking devices, this may prevent more people from having the opportunity to use VR technologies. In this thesis, I explore the process of connecting specific sensor configurations to a VR application in order to be able to customize motion sensors to specific applications, instead of having to rely on solutions that not often match the application needs. The MPU-6050 sensor is very accurate, as it contains 16-bits analog to digital conversion hardware for each channel. Therefore, it captures the x, y, and z channel at the same time. The sensor uses the I2C-bus to interface with the Arduino. The Arduino Ethernet Shield making use of UDP communication procedure provides us a convenient way to interact between Arduino and any desired software application. Finally, three experiments are carried out to demonstrate the application of real-time simulation under virtual environment, and the results show that this work can provide an accurate motion tracking for VR applications in real-time
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