As a method of bare-hand selection, MultiFingerBubble partially addresses the challenges of selecting tiny targets in dense virtual reality (VR) environments. However, due to the Heisenberg Effect and finger flexibility limitations, MultiFingerBubble exhibits constraints such as last-second change of colors and unfriendly gestures. In this paper, we propose Single-Hand Frozen Gestures (SHFG), which stabilizes targets by clenching the fist before selecting, thus preventing the last-second change of colors. To test our design concepts, we develop two user-friendly selection gestures based on SHFG – LiftFinger and PianoFinger. We conduct a user study involving 16 participants and conduct four comparative experiments. Our results suggest that compared with MultiFingerBubble, LiftFinger + SHFG (LiftFrozenFinger) and PianoFinger + SHFG (PianoFrozenFinger) improve accuracy of selection and reduce hand fatigue. Additionally, we apply the two selection methods to select molecular ball-and-stick models to validate their usability. These two techniques could be suitable for other dense virtual reality environments as well.

In numerous applications, an excellent interface design should allow users to perform secondary tasks as naturally as possible without affecting the main task. Multimodal handheld menus are regularly the preferred user interface that meets the natural switching of primary and secondary tasks. However, existing multimodal handheld menus have some limitations under single-handed conditions, or the comfort needs improvement. To address these issues, this paper proposes a novel multimodal handheld menu: Compass+Ring. The “compass” integrates gesture, gaze, and speech into a pie menu, whereas the “ring” serves as a shortcut menu. The Compass menu improves interaction performance and comfortability in one-handed scenarios, and the Ring menu alleviates eye fatigue when both hands are free. We evaluated five handheld menus: Touch, Gaze+Pinch, Speech+Pinch, Bangles, and Compass+Ring. We first analyze the usability of these menus in three different scenarios, and then conduct a user study about these menus in geometry matching and line drawing tasks. The results show that the Bangles menu and the Compass+Ring menu are more suitable for one-handed scenarios than the other three menus, and the Compass+Ring menu is superior to the Bangles menu in terms of efficiency and hand fatigue. In addition, participants indicate that the Ring menu can reduce eye strain for the Compass menu in two-handed scenarios and increase haptic perception.

At present, objective quality assessment methods have significant advantages in video quality assessment (VQA), among which the objective assessment based on deep learning is superior in processing speed and accuracy. However, the objective assessment method has the limitation of poor interpretability of experimental results and lacks the participation of human thinking and experience. In this paper, we present a quality assessment method based on the danmaku visual saliency region. First, to obtain the sentiment trend of the danmaku, we conduct a sentiment analysis of the content of the danmaku. Then, to speed up the video quality assessment, we select the keyframes of the video according to the sentiment analysis. Finally, we innovatively use human visual characteristics to extract salient regions from video keyframes to make the results more subjective. The experiments show that the Pearson correlation coefficient (PCC) and root mean square error (RMSE) between the results of the proposed method and the subjective evaluation are improved, which conforms to the audience's subjective feelings about video viewing, and the assessment results are more accurate.

The analysis of molecular cavities, where ligands interact with protein structures, plays a critical role in protein structure-based drug design. However, it is a challenge because of the ambiguous definition of the cavity boundaries in most cavity detection methods. The cavities are mostly calculated by input parameters, which are difficult for users to visualize cavities in interactive ways. In this paper, we propose a novel method for the interactive exploration of cavity calculation and visualization. Firstly, the proposed method combines the two solvent-excluded surfaces (SES) models of a given protein to define the boundaries and provides cavity emission points. Secondly, the system provides a user-guided interactive method to allow users to select cavities by simply clicking operations and to track the cavity identify and filling process based on position constraints. Finally, the selected cavities are represented with the colorful depth perception method. Experiments show that our work can effectively identify and calculate cavities.