Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications最新文献_第10页

W!NCE: eyewear solution for upper face action units monitoring W !NCE:用于上面部活动单元监测的眼镜解决方案

Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications Pub Date : 2019-06-25 DOI: 10.1145/3314111.3322501

Soha Rostaminia, Alexander Lamson, Subhransu Maji, Tauhidur Rahman, Deepak Ganesan

引用次数: 3

Towards a data-driven framework for realistic self-organized virtual humans: coordinated head and eye movements 面向现实的自组织虚拟人的数据驱动框架:头部和眼睛的协调运动

Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications Pub Date : 2019-06-25 DOI: 10.1145/3314111.3322874

Zhizhuo Yang, Reynold J. Bailey

{"title":"Towards a data-driven framework for realistic self-organized virtual humans: coordinated head and eye movements","authors":"Zhizhuo Yang, Reynold J. Bailey","doi":"10.1145/3314111.3322874","DOIUrl":"https://doi.org/10.1145/3314111.3322874","url":null,"abstract":"Driven by significant investments from the gaming, film, advertising, and customer service industries among others, efforts across many different fields are converging to create realistic representations of humans that look like (computer graphics), sound like (natural language generation), move like (motion capture), and reason like (artificial intelligence) real humans. The ultimate goal of this work is to push the boundaries even further by exploring the development of realistic self-organized virtual humans that are capable of demonstrating coordinated behaviors across different modalities. Eye movements, for example, may be accompanied by changes in facial expression, head orientation, posture, gait properties, or speech. Traditionally however, these modalities are captured and modeled separately and this disconnect contributes to the well-known uncanny valley phenomenon. We focus initially on facial modalities, in particular, coordinated eye and head movements (and eventually facial expressions), but our proposed data-driven framework will be able to accommodate other modalities as well. transfer [Laine et al. 2017]. Despite these advances, the resulting renderings or animations are often still distinguishable from a real human, sometimes in unsettling ways - the so called uncanny valley phenomenon [Mori et al. 2012]. We argue that the traditional approach of capturing and modeling various human modalities separately contributes this effect. In this work, we focus on capturing, transferring, and generating realistic coordinated facial modalities (eye movements, head movements, and eventually facial expressions). We envision a flexible framework that can be extended to accommodate other modalities as well.","PeriodicalId":161901,"journal":{"name":"Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128387257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Gaze awareness improves collaboration efficiency in a collaborative assembly task 注视感知提高了协作装配任务中的协作效率

Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications Pub Date : 2019-06-25 DOI: 10.1145/3317959.3321492

Haofei Wang, Bertram E. Shi

引用次数: 7

Deep learning investigation for chess player attention prediction using eye-tracking and game data 基于眼动追踪和棋局数据的深度学习棋手注意力预测研究

Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications Pub Date : 2019-04-17 DOI: 10.1145/3314111.3319827

Justin Le Louëdec, Thomas Guntz, J. Crowley, D. Vaufreydaz

{"title":"Deep learning investigation for chess player attention prediction using eye-tracking and game data","authors":"Justin Le Louëdec, Thomas Guntz, J. Crowley, D. Vaufreydaz","doi":"10.1145/3314111.3319827","DOIUrl":"https://doi.org/10.1145/3314111.3319827","url":null,"abstract":"This article reports on an investigation of the use of convolutional neural networks to predict the visual attention of chess players. The visual attention model described in this article has been created to generate saliency maps that capture hierarchical and spatial features of chessboard, in order to predict the probability fixation for individual pixels Using a skip-layer architecture of an autoencoder, with a unified decoder, we are able to use multiscale features to predict saliency of part of the board at different scales, showing multiple relations between pieces. We have used scan path and fixation data from players engaged in solving chess problems, to compute 6600 saliency maps associated to the corresponding chess piece configurations. This corpus is completed with synthetically generated data from actual games gathered from an online chess platform. Experiments realized using both scan-paths from chess players and the CAT2000 saliency dataset of natural images, highlights several results. Deep features, pretrained on natural images, were found to be helpful in training visual attention prediction for chess. The proposed neural network architecture is able to generate meaningful saliency maps on unseen chess configurations with good scores on standard metrics. This work provides a baseline for future work on visual attention prediction in similar contexts.","PeriodicalId":161901,"journal":{"name":"Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124151228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

Differential privacy for eye-tracking data 眼动追踪数据的差异隐私

Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications Pub Date : 2019-04-15 DOI: 10.1145/3314111.3319823

Ao Liu, Lirong Xia, A. Duchowski, Reynold J. Bailey, K. Holmqvist, Eakta Jain

引用次数: 49

Eye tracking support for visual analytics systems: foundations, current applications, and research challenges 视觉分析系统的眼动追踪支持:基础、当前应用和研究挑战

Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications Pub Date : 2019-03-03 DOI: 10.1145/3314111.3319919

Nelson Silva, Tanja Blascheck, R. Jianu, Nils Rodrigues, D. Weiskopf, M. Raubal, T. Schreck

引用次数: 15

Privacy-aware eye tracking using differential privacy 使用差分隐私的隐私感知眼动追踪

Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications Pub Date : 2018-12-19 DOI: 10.1145/3314111.3319915

Julian Steil, Inken Hagestedt, Michael Xuelin Huang, A. Bulling

引用次数: 71

PrivacEye: privacy-preserving head-mounted eye tracking using egocentric scene image and eye movement features PrivacEye:保护隐私的头戴式眼动追踪，使用以自我为中心的场景图像和眼动特征

Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications Pub Date : 2018-01-13 DOI: 10.1145/3314111.3319913

Julian Steil, Marion Koelle, Wilko Heuten, Susanne CJ Boll, A. Bulling

引用次数: 59

Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications 第11届美国计算机学会眼动追踪研究与应用研讨会论文集

Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications Pub Date : 1900-01-01 DOI: 10.1145/3314111

引用次数: 6