Traditional unidirectional communication through audio-visual media, such as video and slide, is a universal method of vividly narrating artifacts in museums. This traditional method is evolving with the integration of new technologies to engage visitors, enhancing their experience and learning. In recent years, eye-controlled interaction has been used to enrich traditional exhibitions, especially of ancient paintings. This article presents an eye-controlled application for actively viewing the audio-visual media of a traditional Chinese painting. Users can trigger animations and audio narrations by focusing their gaze on specific areas of the screen. To evaluate the value of experience and learning in integrating audio-visual media with eye-controlled interaction, we compared Eye-Controlled Application with the two baseline conditions (Video and Slide) through a comparison experiment (N = 40) in terms of user experience, learning outcome, cognitive load, intrinsic motivation, and visual behavior. The findings indicated the positive effects of Eye-Controlled Application on experience and learning, especially understanding. Users had stronger intrinsic motivation using Eye-Controlled Application, which contributed to positive learning outcomes. We found that Eye-Controlled Application changed visual behavior by increasing the proportion of gaze patterns and decreasing the proportion of movement patterns and scan patterns. The alteration in visual behavior primarily stemmed from the eye-controlled interaction, which directed the user’s focus toward specific areas, thereby guiding their attention to the necessary content. Additionally, the qualitative results suggested that each of the three conditions had its advantages and disadvantages. Importantly, the condition with Eye-Controlled Application carried potential risks of distraction, reduced efficiency, and increased burden. This study contributes to the potential of eye-controlled interaction for enhancing visitors’ experience and learning, providing new insights into the development of interactive audio-visual media within museums.

Gaze interaction in virtual reality (VR) offers promising advantages in speed and hands-free operation. However, selecting a suitable selection mechanism in VR interfaces with different visual encodings remains a challenge. This research compares two visual input techniques and explores specific visual elements, including field of view (FOV), design style, and number of targets, with the goal of alleviating these problems. The pilot study identifies two search stages, and it also demonstrates a significant effect of FOV on search time. In the formal experiment, a gaze-based trigger experiment is employed to examine the selection performance of fixation and smooth pursuit. The results reveal that optimizing FOV and number of targets improves the triggering efficiency. Furthermore, fixation outperforms pursuit in terms of triggering time and accuracy under the same conditions, while cognitive load during triggering process remains relatively similar. These findings are expected to propose design recommendations to enhance fixation and pursuit within VR interfaces.

Interface icons are often scaled to adapt to different displays in cross-device collaborations. However, adaptive scaling of icons may cause perceptual bias in how icon arrays are visually perceived, which reduces usability and coherent user experience. This article presents an empirical study that evaluates the perceptual bias in the consistency of icon spacing and size caused by adaptive scaling. Then the impact of various visual features of icons (i.e., the border shape, polarity, and composition) on the perceptual bias are investigated. In this study, we found that cross-device scaling of icons causes a perceptual bias in the consistency of icon spacing and size. The shape of the icon border has a significant difference in the perceived spacing of icons, and the bias of the round shape is smaller than that of the square shape. Moreover, changing the icon polarity can affect the perceptual bias of consistency in icon size. These findings are expected to propose scaling recommendations for improving the visual consistency of icon arrays across.

In the field of digital humanities, color research aims to discover explanations for painting history and color usage habits. However, researchers analyzing color relationships is challenging and time-consuming, as it requires color extraction and a detailed review of many painting images for reference and comparison of color relationships. In our work, we propose ColorNetVis, an interactive color network analysis tool that enables researchers to explore color relationships through color networks. The core of ColorNetVis is a bipartite network model that establishes a bipartite relationship between colors and Chinese painting within a scope based on color difference measurement. It constructs a one-mode color network through projection algorithms and similarity calculation methods to discover the relationship between colors. We propose a coordinated set of views to demonstrate the combination of determined color networks with painting types and real-world attributes. We use color space view, color attribute distribution view, and single color query components to assist researchers in conducting detailed color analysis and validation. Through case studies, researcher reviews, and user studies, we demonstrate that ColorNetVis can effectively help researchers discover knowledge of color relationships and potential color research directions.

Color restoration of ancient Chinese paintings plays a significant role in Chinese culture protection and inheritance. However, traditional color restoration is challenging and time-consuming because it requires professional restorers to conduct detailed literature reviews on numerous paintings for reference colors. After that, they have to fill in the inferred colors on the painting manually. In this paper, we present PColorizor, an interactive system that integrates advanced deep-learning models and novel visualizations to ease the difficulties of color restoration. PColorizor is established on the principle of poem-painting congruence. Given a color-faded painting, we employ both explicit and implicit color guidance implied by ideorealm-congruent poems to associate reference paintings. We propose a mountain-like visualization to facilitate efficient navigation of the color schemes extracted from the reference paintings. This visual representation allows users to easily see the color distribution over time at both the ideorealm and imagery levels. Moreover, we demonstrate the ideorealm understood by deep learning models through visualizations to bridge the communication gap between human restorers and deep learning models. We also adopt intelligent color-filling techniques to accelerate manual color restoration further. To evaluate PColorizor, we collaborate with domain experts to conduct two case studies to collect their feedback. The results suggest that PColorizor could be beneficial in enabling the effective restoration of color-faded paintings.