Performance Analysis of Virtual Human Bodies with Clothing and Hair from Images to Animation

2018 ◽  
Vol 8 (6) ◽  
pp. 69
Author(s):  
Kulalvaimozhi. V. P. ◽  
Germanus Alex. M ◽  
John Peter. S
Keyword(s):  
Machine Learning ◽  
Performance Analysis ◽  
Camera Calibration ◽  
Pose Estimation ◽  
Body Shape ◽  
Data Driven ◽  
Virtual Human ◽  
Single Image ◽  
Shape Estimation ◽  
Challenging Situation

Virtual human bodies, clothing, and hair are widely used in a number of scenarios such as 3D animated movies, gaming, and online fashion. Machine learning can be used to construct data-driven 3D human bodies, clothing, and hair. In this thesis, we provide a solution to 3D shape and pose estimation under the most challenging situation where only a single image is available and the image is captured in a natural environment with unknown camera calibration. We also demonstrate that a simplified 2D clothing model helps to increase the accuracy of 2D body shape estimation significantly.

Performance analysis on liquid-cooled battery thermal management for electric vehicles based on machine learning

2021 ◽  
Vol 494 ◽  
pp. 229727
Author(s):  
Xingwang Tang ◽  
Qin Guo ◽  
Ming Li ◽  
Changhua Wei ◽  
Zhiyao Pan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Performance Analysis ◽  
Thermal Management ◽  
Electric Vehicles ◽  
Battery Thermal Management

scholarly journals Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems

2021 ◽  
Author(s):  
Ekaterina Kochmar ◽  
Dung Do Vu ◽  
Robert Belfer ◽  
Varun Gupta ◽  
Iulian Vlad Serban ◽  
...  
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Language Processing ◽  
Intelligent Tutoring Systems ◽  
Large Scale ◽  
Intelligent Tutoring ◽  
Performance Outcomes ◽  
Data Driven ◽  
Personalized Feedback ◽  
Tutoring Systems

AbstractIntelligent tutoring systems (ITS) have been shown to be highly effective at promoting learning as compared to other computer-based instructional approaches. However, many ITS rely heavily on expert design and hand-crafted rules. This makes them difficult to build and transfer across domains and limits their potential efficacy. In this paper, we investigate how feedback in a large-scale ITS can be automatically generated in a data-driven way, and more specifically how personalization of feedback can lead to improvements in student performance outcomes. First, in this paper we propose a machine learning approach to generate personalized feedback in an automated way, which takes individual needs of students into account, while alleviating the need of expert intervention and design of hand-crafted rules. We leverage state-of-the-art machine learning and natural language processing techniques to provide students with personalized feedback using hints and Wikipedia-based explanations. Second, we demonstrate that personalized feedback leads to improved success rates at solving exercises in practice: our personalized feedback model is used in , a large-scale dialogue-based ITS with around 20,000 students launched in 2019. We present the results of experiments with students and show that the automated, data-driven, personalized feedback leads to a significant overall improvement of 22.95% in student performance outcomes and substantial improvements in the subjective evaluation of the feedback.

Sign in / Sign up

Export Citation Format

Share Document