Gaze control is a substitution for disabled people to play computer games. However, many disabled people may be inexperienced in games and/or novices using gaze-control. This study presents a game accessibility approach using gaze control modality for novice players and disabled people. A workshop was conducted involving a playtest on three games with gaze-control. The game experiences were observed, recorded, and evaluated with mixed methods. The study estimated the gaze control game accessibility by System Usability Scale (SUS), Game Experience Questionnaire (GEQ), and an open-ended questionnaire. The gaze control modality demonstrated possible game accessibility to people with motor disabilities. The results also indicate that the challenge of game mechanics and the accuracy of the gaze-control system are two significant impact factors. Further research will be conducted on gaze-control games including more disabled people, and also develop the data analysis methods for evaluating gaze-control modality for game accessibility.

Eye tracking systems are crucial methods by which motor disabled people can interact with computers. Previous research in this field has identified various accessibility affecting eye tracking technologies and applications. However, there is limited research into first-hand user experiences among individuals with motor disabilities. This study aims to examine the actual challenges with eye tracking systems and the gaze interaction faced by motor disabled people. A survey was conducted among people with motor disabilities who used eye trackers for computer interactions. It reveals the current issues from their first-hand experiences in three areas: eye tracking program, gaze interaction, and accessible applications. A knowledge graph arising from the survey delineates the connections among the eye tracking usability issues. The survey’s results also indicate practical strategies for future improvements in eye trackers. 

EyeCompass is a novel free-eye drawing system enabling high-fidelity and efficient free-eye drawing through unimodal gaze control, addressing the bottlenecks of gaze-control drawing. EyeCompass helps people to draw using only their eyes, which is of value to people with motor disabilities. Currently, there is no effective gaze-control drawing application due to multiple challenges including involuntary eye movements, conflicts between visuomotor transformation and ocular observation, gaze trajectory control, and inherent eye-tracking errors. EyeCompass addresses this using two initial gaze-control drawing mechanisms: brush damping dynamics and the gaze-oriented method. The user experiments compare the existing gaze-control drawing method and EyeCompass, showing significant improvements in the drawing performance of the mechanisms concerned. The field study conducted with motor-disabled people produced various creative graphics and indicates good usability of the system. Our studies indicate that EyeCompass is a high-fidelity, accurate, feasible free-eye drawing method for creating artistic works via unimodal gaze control.

Some people with a motor disability that limits hand movements use technology to draw via their eyes. Free-eye drawing has been re-investigated recently and yielded state-of-the-art results via unimodal gaze control. However, limitations remain, including limited functions, conflicts between observation and drawing, and the brush tailing issue. We introduce a professional unimodal gaze control free-eye drawing application and improve upon free-eye drawing by extended gaze-based user interface functions, improved brush dynamics, and a double-blink gaze gesture. An experiment and a field study were conducted to assess the system’s usability compared to the mainstream gaze-control drawing method and hand drawing and the accessibility among users with motor disabilities. The results showed that the application provides efficient interaction and the ability to create hand-sketch-level graphics for people with motor disabilities. Herein, we contribute a robust and professional free-eye drawing application, detailing valuable design considerations for future developments in gaze interaction.

Synthesizing realistic clouds is a complex and demanding task, as clouds are characterized by random shapes, complex scattering and turbulent appearances. Existing approaches either employ two-dimensional image matting or three-dimensional physical simulations. This paper proposes a novel sketch-to-image deep learning system using fast sketches to paint and edit volumetric clouds. We composed a dataset of 2000 real cloud images and translated simple strokes into authentic clouds based on a conditional generative adversarial network (cGAN). Compared to previous cloud simulation methods, our system demonstrates more efficient and straightforward processes to generate authentic clouds for computer graphics, providing a widely accessible sky scene design approach for use by novices, amateurs, and expert artists.