scholarly journals Real-time Character Motion Effect Enhancement Based on Fluid Simulation

2012 ◽  
Vol 11 (1) ◽  
pp. 59-68
Author(s):  
Tianchen Xu ◽  
Enhua Wu ◽  
Mo Chen ◽  
Ming Xie

In fast figure animation, motion blur is of crucial importance, and this is especially true when an artist wants to generate exaggerating effect through figure motion. For a quite long period of time, animators seek the answer by using certain kind of image blending, no matter by the means of hardware or software. In recent years, methods based on 3D geometry of the motion figure with global illumination become gradually in demand, as they could deliver relatively high quality of motion blur effect. However, the computation cost in those methods is always very high, thus real time rendering become quite difficult to achieve. In this paper, a real-time motion effect based on 3D geometric approach is proposed, in which a special effect along the motion trajectory based on fluid simulation is combined with the volumetric motion blur. Furthermore, the motion trajectory would be decomposed and multi-pass geometry rendering would be employed to achieve geometry instancing for reuse. In this manner, the redundant calculation of each frame could be avoided, and the limitation of trajectory generation would be broken. In the pipeline, we separate motion tracking and fluid solution, to support various fluid effects flexibly. The scheme we present makes use of GPU geometry shading in parallel, aiming at guaranteeing high efficiency of computation while delivering splendid rendering. As a result, real time rendering including the motion blur effect is achieved.

2008 ◽  
Vol 4 (4) ◽  
pp. 339-347 ◽  
Author(s):  
Xiaojun Chen ◽  
Yanping Lin ◽  
Yiqun Wu ◽  
Chengtao Wang

Author(s):  
Zahari Taha ◽  
Mohd Yashim Wong ◽  
Hwa Jen Yap ◽  
Amirul Abdullah ◽  
Wee Kian Yeo

Immersion is one of the most important aspects in ensuring the applicability of Virtual Reality systems to training regimes aiming to improve performance. To ensure that this key aspect is met, the registration of motion between the real world and virtual environment must be made as accurate and as low latency as possible. Thus, an in-house developed Inertial Measurement Unit (IMU) system is developed for use in tracking the movement of the player’s racquet. This IMU tracks 6 DOF motion data and transmits it to the mobile training system for processing. Physically, the custom motion is built into the shape of a racquet grip to give a more natural sensation when swinging the racquet. In addition to that, an adaptive filter framework is also established to cope with different racquet movements automatically, enabling real-time 6 DOF tracking by balancing the jitter and latency. Experiments are performed to compare the efficacy of our approach with other conventional tracking methods such as the using Microsoft Kinect. The results obtained demonstrated noticeable accuracy and lower latency when compared with the aforementioned methods.


Algorithms ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 266
Author(s):  
Lorenzo Rapetti ◽  
Yeshasvi Tirupachuri ◽  
Kourosh Darvish ◽  
Stefano Dafarra ◽  
Gabriele Nava ◽  
...  

This paper contributes towards the development of motion tracking algorithms for time-critical applications, proposing an infrastructure for dynamically solving the inverse kinematics of highly articulate systems such as humans. The method presented is model-based, it makes use of velocity correction and differential kinematics integration in order to compute the system configuration. The convergence of the model towards the measurements is proved using Lyapunov analysis. An experimental scenario, where the motion of a human subject is tracked in static and dynamic configurations, is used to validate the inverse kinematics method performance on human and humanoid models. Moreover, the method is tested on a human-humanoid retargeting scenario, verifying the usability of the computed solution in real-time robotics applications. Our approach is evaluated both in terms of accuracy and computational load, and compared to iterative optimization algorithms.


Sign in / Sign up

Export Citation Format

Share Document