1,656 research outputs found
High-fidelity rendering on shared computational resources
The generation of high-fidelity imagery is a computationally expensive process
and parallel computing has been traditionally employed to alleviate this cost.
However, traditional parallel rendering has been restricted to expensive shared
memory or dedicated distributed processors. In contrast, parallel computing on
shared resources such as a computational or a desktop grid, offers a low cost alternative. But, the prevalent rendering systems are currently incapable of seamlessly handling such shared resources as they suffer from high latencies, restricted
bandwidth and volatility. A conventional approach of rescheduling failed jobs in
a volatile environment inhibits performance by using redundant computations.
Instead, clever task subdivision along with image reconstruction techniques provides an unrestrictive fault-tolerance mechanism, which is highly suitable for
high-fidelity rendering. This thesis presents novel fault-tolerant parallel rendering algorithms for effectively tapping the enormous inexpensive computational
power provided by shared resources.
A first of its kind system for fully dynamic high-fidelity interactive rendering
on idle resources is presented which is key for providing an immediate feedback
to the changes made by a user. The system achieves interactivity by monitoring
and adapting computations according to run-time variations in the computational
power and employs a spatio-temporal image reconstruction technique for enhancing the visual fidelity. Furthermore, algorithms described for time-constrained offline rendering of still images and animation sequences, make it possible to deliver
the results in a user-defined limit. These novel methods enable the employment
of variable resources in deadline-driven environments
The Game FAVR: A Framework for the Analysis of Visual Representation in Video Games
This paper lays out a unified framework of the ergodic animage, the rule-based and interactiondriven part of visual representation in video games. It is the end product of a three-year research project conducted by the INTEGRAE team, and is divided into three parts. Part 1 contextualizes the research on graphics and visuality within game studies, notably through the opposition between fiction and rules and the difficulties in finding common vocabulary to discuss key visual concepts such as perspective and point of view. Part 2 discusses a number of visual traditions through which we frame video game graphics (film, animation, art history, graphical projection and technical drawing), highlighting their relevance and shortcomings in addressing the long history of video games and the very different paradigms of 2D and 3D graphics. Part 3 presents the Game FAVR, a model that allows any game’s visual representation to be described and discussed through a common frame and vocabulary. The framework is presented in an accessible manner and is organized as a toolkit, with sample case studies, templates, and a flowchart for using the FAVR provided as an annex, so that researchers and students can immediately start using it
XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera
We present a real-time approach for multi-person 3D motion capture at over 30
fps using a single RGB camera. It operates successfully in generic scenes which
may contain occlusions by objects and by other people. Our method operates in
subsequent stages. The first stage is a convolutional neural network (CNN) that
estimates 2D and 3D pose features along with identity assignments for all
visible joints of all individuals.We contribute a new architecture for this
CNN, called SelecSLS Net, that uses novel selective long and short range skip
connections to improve the information flow allowing for a drastically faster
network without compromising accuracy. In the second stage, a fully connected
neural network turns the possibly partial (on account of occlusion) 2Dpose and
3Dpose features for each subject into a complete 3Dpose estimate per
individual. The third stage applies space-time skeletal model fitting to the
predicted 2D and 3D pose per subject to further reconcile the 2D and 3D pose,
and enforce temporal coherence. Our method returns the full skeletal pose in
joint angles for each subject. This is a further key distinction from previous
work that do not produce joint angle results of a coherent skeleton in real
time for multi-person scenes. The proposed system runs on consumer hardware at
a previously unseen speed of more than 30 fps given 512x320 images as input
while achieving state-of-the-art accuracy, which we will demonstrate on a range
of challenging real-world scenes.Comment: To appear in ACM Transactions on Graphics (SIGGRAPH) 202
XNect: Real-time Multi-person 3D Human Pose Estimation with a Single RGB Camera
We present a real-time approach for multi-person 3D motion capture at over 30 fps using a single RGB camera. It operates in generic scenes and is robust to difficult occlusions both by other people and objects. Our method operates in subsequent stages. The first stage is a convolutional neural network (CNN) that estimates 2D and 3D pose features along with identity assignments for all visible joints of all individuals. We contribute a new architecture for this CNN, called SelecSLS Net, that uses novel selective long and short range skip connections to improve the information flow allowing for a drastically faster network without compromising accuracy. In the second stage, a fully-connected neural network turns the possibly partial (on account of occlusion) 2D pose and 3D pose features for each subject into a complete 3D pose estimate per individual. The third stage applies space-time skeletal model fitting to the predicted 2D and 3D pose per subject to further reconcile the 2D and 3D pose, and enforce temporal coherence. Our method returns the full skeletal pose in joint angles for each subject. This is a further key distinction from previous work that neither extracted global body positions nor joint angle results of a coherent skeleton in real time for multi-person scenes. The proposed system runs on consumer hardware at a previously unseen speed of more than 30 fps given 512x320 images as input while achieving state-of-the-art accuracy, which we will demonstrate on a range of challenging real-world scenes
Parallelizing RRT on large-scale distributed-memory architectures
This paper addresses the problem of parallelizing the Rapidly-exploring Random Tree (RRT) algorithm on large-scale distributed-memory architectures, using the Message Passing Interface. We compare three parallel versions of RRT based on classical parallelization schemes. We evaluate them on different motion planning problems and analyze the various factors influencing their performance
Accelerated Quality-Diversity through Massive Parallelism
Quality-Diversity (QD) optimization algorithms are a well-known approach to
generate large collections of diverse and high-quality solutions. However,
derived from evolutionary computation, QD algorithms are population-based
methods which are known to be data-inefficient and requires large amounts of
computational resources. This makes QD algorithms slow when used in
applications where solution evaluations are computationally costly. A common
approach to speed up QD algorithms is to evaluate solutions in parallel, for
instance by using physical simulators in robotics. Yet, this approach is
limited to several dozen of parallel evaluations as most physics simulators can
only be parallelized more with a greater number of CPUs. With recent advances
in simulators that run on accelerators, thousands of evaluations can now be
performed in parallel on single GPU/TPU. In this paper, we present QDax, an
accelerated implementation of MAP-Elites which leverages massive parallelism on
accelerators to make QD algorithms more accessible. We show that QD algorithms
are ideal candidates to take advantage of progress in hardware acceleration. We
demonstrate that QD algorithms can scale with massive parallelism to be run at
interactive timescales without any significant effect on the performance.
Results across standard optimization functions and four neuroevolution
benchmark environments shows that experiment runtimes are reduced by two
factors of magnitudes, turning days of computation into minutes. More
surprising, we observe that reducing the number of generations by two orders of
magnitude, and thus having significantly shorter lineage does not impact the
performance of QD algorithms. These results show that QD can now benefit from
hardware acceleration, which contributed significantly to the bloom of deep
learning
Virtual Reality Games for Motor Rehabilitation
This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion
Perception of space through representation media: a comparison between 2D representation techniques and 3D virtual environments
Thesis (Master)--İzmir Institute of Technology, Architecture, İzmir, 2005Includes bibliographical references (leaves: 109-113)Text in English; Abstract: Turkish and Englishxii, 122 leavesFor centuries, 2D drawing techniques such as plans, sections and elevations have been the main communication media for the profession of architecture. Addition to these techniques, for two decades, computer based representation techniques and 3D virtual environments (VE) have also entered to the profession of architecture. Effects of these computer based techniques on perception of space have always been interrogated by several researches.Although these researches generally regarded these computerized techniques as better and proper than conventional techniques, in some cases conventional techniques can be more effective to depict architectural space. Main aim of this thesis is to compare and evaluate the positive effects and shortcomings of 3D virtual environments and 2D conventional representation techniques in the context of perception of architectural space. Parallel to this objective, the thesis also aims to show the differentiation in perception of space with the change of representation media. To show these differences, a comparative method is used. As the main step of the application of this method, an experimental case study and survey has been constituted for comparing 2D conventional techniques and 3D computer based techniques. In this survey, 38 first yearstudents from Izmir Institute of technology have taken place as test subject.According to the results of this comparative case study, contributions and shortcomings of 2D conventional representation techniques and 3D computer based techniques on improving the capability of architects on perception of the space have been determined
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