Garment Simulation and Collision Detection on a Mobile Device

2016 ◽  
Vol 7 (3) ◽  
pp. 1-15 ◽  
Author(s):  
Tzvetomir Ivanov Vassilev
Keyword(s):  
Parallel Computing ◽  
Mobile Device ◽  
Human Body ◽  
Collision Detection ◽  
Gpu Computing ◽  
Smart Phone ◽  
Multicore Cpu ◽  
Interference Tests ◽  
Mass Spring ◽  
Cloth Draping

This paper describes several techniques for accelerating a virtual try-on garment simulation on a mobile device (smartphone or tablet) using parallel computing on a multicore CPU, GPU computing or both depending on the mobile hardware. The system exploits a mass-spring cloth model with velocity modification approach to overcome the super-elasticity. The simulation starts from flat garment pattern meshes positioned around a 3D human body, then seaming forces are applied on the edges of the panels until the garment is seamed and several cloth draping steps are performed in the end. The cloth-body collision detection and response algorithm is based on image-space interference tests and the cloth-cloth collision detection uses entirely GPU based approach on the newer hardware or recursive parallel algorithm on the CPU. As the results section shows the average time of dressing a virtual body with a garment on a modern smart phone supporting OpenGL ES2.0 is 2 seconds and on a tablet supporting OpenGL ES3.0 or 3.1 is less than one second.

scholarly journals Data-Driven 3-D Human Body Customization With a Mobile Device

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 27939-27948 ◽  
Author(s):  
Dan Song ◽  
Ruofeng Tong ◽  
Jiang Du ◽  
Yun Zhang ◽  
Yao Jin
Keyword(s):  
Mobile Device ◽  
Human Body ◽  
Data Driven

Parallel computing for fringe pattern processing: A multicore CPU approach in MATLAB® environment

2009 ◽  
Vol 47 (11) ◽  
pp. 1286-1292 ◽  
Author(s):  
Wenjing Gao ◽  
Qian Kemao ◽  
Haixia Wang ◽  
Feng Lin ◽  
Hock Soon Seah
Keyword(s):  
Parallel Computing ◽  
Fringe Pattern ◽  
Pattern Processing ◽  
Multicore Cpu

COLLISION DETECTION FOR CLOTH SIMULATION USING BOUNDING SPHERE HIERARCHY

2015 ◽  
Vol 75 (2) ◽  
Author(s):  
Abdullah Bade ◽  
Ching Sue Ping ◽  
Siti Hasnah Tanalol
Keyword(s):  
Collision Detection ◽  
Detection System ◽  
Computational Cost ◽  
Detection Algorithm ◽  
Frame Rate ◽  
Generation Process ◽  
Cloth Simulation ◽  
Real Time System ◽  
Simple Mass ◽  
Mass Spring

For the past 2-decades, the challenges of collision detection on cloth simulation have attracted numerous researchers.  Simple mass spring model is used to model the cloth where the movement of the particles within the cloth was controlled by applying the Newton’s second law. After the modeling stage, implementation of the collision detection algorithm took place on cloth has been done. The collision detection technique used is bounding sphere hierarchy. Then, quad tree is being used to partitioning the bounding sphere and the collision search was based on the top-down approach. A prototype of the collision detection system is developed on cloth simulation and several experiments were conducted. Time taken for this system to be executed is around 235.258 milliseconds. Then the frame rate is at the average of 22 frames per second which is close to the real time system. Times taken for the collision detection system travels from root to nodes were 23 seconds. As a conclusion, the computational cost for bounding sphere hierarchy is much higher because the bounding sphere required more vertices for generation process, however the execution time for bounding sphere hierarchy is faster than the AABB hierarchy.  

scholarly journals ANDROID APPLICATION FOR MEASURING HUMAN BODY TEMPERATURE AND FORCASTING FUTURE ILLNESS

2014 ◽  
pp. 161-164
Author(s):  
S PRABHAKARAN ◽  
DHANESHWARI KUMARI ◽  
RIA AHUJA
Keyword(s):  
Operating System ◽  
Body Temperature ◽  
Mobile Device ◽  
Human Body ◽  
The Body ◽  
The Other ◽  
Android Application ◽  
Human Body Temperature ◽  
Normal Human ◽  
Android Operating System

Android Application for measuring human body temperature is a new age mobile thermometer. This kind of application already exists but requires manual feeding temperature. In our project, we propose an application which will measure the body temperature automatically while the user is operating the mobile device. It has an in-built function which can trigger alert messages whenever the temperature becomes critical more than normal human body temperature. The display segment of the device is made up of capacitive touch screen, which can act upon the bioelectricity produced by human body with each and every touch. This application requires Android Operating System Version 2.2. It will also diagnose the other diseases the user might have depending upon the symptoms entered.

scholarly journals Hybrid MPI and CUDA Parallelization for CFD Applications on Multi-GPU HPC Clusters

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Jianqi Lai ◽  
Hang Yu ◽  
Zhengyu Tian ◽  
Hua Li
Keyword(s):  
Parallel Computing ◽  
Parallel Algorithm ◽  
Graphics Processing Units ◽  
Message Passing Interface ◽  
Gpu Computing ◽  
Programming Model ◽  
Domain Decomposition Method ◽  
Parallel Execution ◽  
Performance Measurements ◽  
Cfd Applications

Graphics processing units (GPUs) have a strong floating-point capability and a high memory bandwidth in data parallelism and have been widely used in high-performance computing (HPC). Compute unified device architecture (CUDA) is used as a parallel computing platform and programming model for the GPU to reduce the complexity of programming. The programmable GPUs are becoming popular in computational fluid dynamics (CFD) applications. In this work, we propose a hybrid parallel algorithm of the message passing interface and CUDA for CFD applications on multi-GPU HPC clusters. The AUSM + UP upwind scheme and the three-step Runge–Kutta method are used for spatial discretization and time discretization, respectively. The turbulent solution is solved by the K−ω SST two-equation model. The CPU only manages the execution of the GPU and communication, and the GPU is responsible for data processing. Parallel execution and memory access optimizations are used to optimize the GPU-based CFD codes. We propose a nonblocking communication method to fully overlap GPU computing, CPU_CPU communication, and CPU_GPU data transfer by creating two CUDA streams. Furthermore, the one-dimensional domain decomposition method is used to balance the workload among GPUs. Finally, we evaluate the hybrid parallel algorithm with the compressible turbulent flow over a flat plate. The performance of a single GPU implementation and the scalability of multi-GPU clusters are discussed. Performance measurements show that multi-GPU parallelization can achieve a speedup of more than 36 times with respect to CPU-based parallel computing, and the parallel algorithm has good scalability.

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