In order to further enhance the aesthetics,artistry and culture of interior design,this paper mainly studies and analyzes the application strategies of traditional Chinese cultural elements in interior design,in order...In order to further enhance the aesthetics,artistry and culture of interior design,this paper mainly studies and analyzes the application strategies of traditional Chinese cultural elements in interior design,in order to provide reference for future developments.展开更多
L3 automated driving has introduced a trend of drivers engaging in non-driving-related tasks(NDRTs),but it also poses safety challenges for reconstructing drivers’situation awareness(SA).Two consecutive empirical stu...L3 automated driving has introduced a trend of drivers engaging in non-driving-related tasks(NDRTs),but it also poses safety challenges for reconstructing drivers’situation awareness(SA).Two consecutive empirical studies in a driving simulator were conducted to investigate the effect of two peripheral interactions(airflow conveying the intended behaviors of vehicles and surround sound conveying the information of road users)on drivers’SA performance,NDRT efficiency,workload,and user experience.The first study(n=21)explored the differential effects of airflow,surround sound,and their integration.The second study(n=30)investigated how the integrated interaction performed across different NDRT difficulties.Results demonstrated that airflow and surround sound could significantly improve drivers’SA when used individually,each having distinct advantages.The integration of these two interactions yielded the best results.Notably,the integrated interaction showed greater effectiveness in improving SA during hard NDRT compared to the easy one.Furthermore,drivers reported reduced subjective workloads and enhanced user experience when leveraging these peripheral interaction methods.Our work offers insights for designing in-vehicle interaction systems that not only reconstruct drivers’SA but also support NDRT participation,ensuring safety and productivity.展开更多
Images are widely used by companies to advertise their products and promote awareness of their brands.The automatic synthesis of advertising images is challenging because the advertising message must be clearly convey...Images are widely used by companies to advertise their products and promote awareness of their brands.The automatic synthesis of advertising images is challenging because the advertising message must be clearly conveyed while complying with the style required for the product,brand,or target audience.In this study,we proposed a data-driven method to capture individual design attributes and the relationships between elements in advertising images with the aim of automatically synthesizing the input of elements into an advertising image according to a specified style.To achieve this multi-format advertisement design,we created a dataset containing 13280 advertising images with rich annotations that encompassed the outlines and colors of the elements,in addition to the classes and goals of the advertisements.Using our probabilistic models,users guided the style of synthesized advertisements via additional constraints(e.g.,context-based keywords).We applied our method to a variety of design tasks,and the results were evaluated in several perceptual studies,which showed that our method improved users’satisfaction by 7.1%compared to designs generated by nonprofessional students,and that more users preferred the coloring results of our designs to those generated by the color harmony model and Colormind.展开更多
Artificial intelligence generated content(AIGC)has been a research hotspot in the field of artificial intelligence in recent years.It is expected to replace humans in performing some of the work of content generation ...Artificial intelligence generated content(AIGC)has been a research hotspot in the field of artificial intelligence in recent years.It is expected to replace humans in performing some of the work of content generation at a low cost and a high volume,such as music,painting,multimodal content generation,news articles,summary reports,stock commentary summaries,and even content and digital people generated in the meta-universe.AIGC provides a new technical path for the development and implementation of AI in the future.展开更多
Predicting visual attention facilitates an adaptive virtual museum environment and provides a context-aware and interactive user experience.Explorations toward development of a visual attention mechanism using eye-tra...Predicting visual attention facilitates an adaptive virtual museum environment and provides a context-aware and interactive user experience.Explorations toward development of a visual attention mechanism using eye-tracking data have so far been limited to 2D cases,and researchers are yet to approach this topic in a 3D virtual environment and from a spatiotemporal perspective.We present the first 3D Eye-tracking Dataset for Visual Attention modeling in a virtual Museum,known as the EDVAM.In addition,a deep learning model is devised and tested with the EDVAM to predict a user’s subsequent visual attention from previous eye movements.This work provides a reference for visual attention modeling and context-aware interaction in the context of virtual museums.展开更多
文摘In order to further enhance the aesthetics,artistry and culture of interior design,this paper mainly studies and analyzes the application strategies of traditional Chinese cultural elements in interior design,in order to provide reference for future developments.
基金supported by the National Natural Science Foundation of China(No.62407038).
文摘L3 automated driving has introduced a trend of drivers engaging in non-driving-related tasks(NDRTs),but it also poses safety challenges for reconstructing drivers’situation awareness(SA).Two consecutive empirical studies in a driving simulator were conducted to investigate the effect of two peripheral interactions(airflow conveying the intended behaviors of vehicles and surround sound conveying the information of road users)on drivers’SA performance,NDRT efficiency,workload,and user experience.The first study(n=21)explored the differential effects of airflow,surround sound,and their integration.The second study(n=30)investigated how the integrated interaction performed across different NDRT difficulties.Results demonstrated that airflow and surround sound could significantly improve drivers’SA when used individually,each having distinct advantages.The integration of these two interactions yielded the best results.Notably,the integrated interaction showed greater effectiveness in improving SA during hard NDRT compared to the easy one.Furthermore,drivers reported reduced subjective workloads and enhanced user experience when leveraging these peripheral interaction methods.Our work offers insights for designing in-vehicle interaction systems that not only reconstruct drivers’SA but also support NDRT participation,ensuring safety and productivity.
基金Project supported by the National Science and Technology Innovation 2030 Major Project of the Ministry of Science and Technology of China(No.2018AAA0100700)the National Natural Science Foundation of China(No.61672451)+2 种基金the Provincial Key Research and Development Plan of Zhejiang Province,China(No.2019C03137)the China Postdoctoral Science Foundation(No.2018M630658)the Alibaba-Zhejiang University Joint Institute of Frontier Technologies。
文摘Images are widely used by companies to advertise their products and promote awareness of their brands.The automatic synthesis of advertising images is challenging because the advertising message must be clearly conveyed while complying with the style required for the product,brand,or target audience.In this study,we proposed a data-driven method to capture individual design attributes and the relationships between elements in advertising images with the aim of automatically synthesizing the input of elements into an advertising image according to a specified style.To achieve this multi-format advertisement design,we created a dataset containing 13280 advertising images with rich annotations that encompassed the outlines and colors of the elements,in addition to the classes and goals of the advertisements.Using our probabilistic models,users guided the style of synthesized advertisements via additional constraints(e.g.,context-based keywords).We applied our method to a variety of design tasks,and the results were evaluated in several perceptual studies,which showed that our method improved users’satisfaction by 7.1%compared to designs generated by nonprofessional students,and that more users preferred the coloring results of our designs to those generated by the color harmony model and Colormind.
文摘Artificial intelligence generated content(AIGC)has been a research hotspot in the field of artificial intelligence in recent years.It is expected to replace humans in performing some of the work of content generation at a low cost and a high volume,such as music,painting,multimodal content generation,news articles,summary reports,stock commentary summaries,and even content and digital people generated in the meta-universe.AIGC provides a new technical path for the development and implementation of AI in the future.
基金Project supported by the National Natural Science Foundation of China(No.61802341)the National Science and Technology Innovation 2030 Major Project of the Ministry of Science and Technology of China(No.2018AAA0100703)the Research Innovation Plan of the Ministry of Education of China,and the Provincial Key Research and Development Plan of Zhejiang Province,China(No.2019C03137)。
文摘Predicting visual attention facilitates an adaptive virtual museum environment and provides a context-aware and interactive user experience.Explorations toward development of a visual attention mechanism using eye-tracking data have so far been limited to 2D cases,and researchers are yet to approach this topic in a 3D virtual environment and from a spatiotemporal perspective.We present the first 3D Eye-tracking Dataset for Visual Attention modeling in a virtual Museum,known as the EDVAM.In addition,a deep learning model is devised and tested with the EDVAM to predict a user’s subsequent visual attention from previous eye movements.This work provides a reference for visual attention modeling and context-aware interaction in the context of virtual museums.
