Recently, the electroencephalogram (EEG) has been widely adopted as a quantitative indicator for monitoring emotional modulation during virtual reality (VR) experiences. Although VR emotion-induction materials are continuously being developed, few methods have been proposed for constructing VR scenarios through psychophysiological calibration. To achieve precise emotional regulation, we propose an integrated psychophysiological oriented EEG-based VR scenario modeling approach for emotion induction. This methodology constructs 3D VR scenarios by extending 2D elements through core processes: (1) deconstruction of emotion-annotated 2D images to extract visual-audio emotional patterns, (2) development of immersive environments with dynamic camera trajectories, and (3) integration of audio stimuli and real-world physical configurations to form multi-sensory emotional stimuli. During emotioninduction experiments, EEG signals reflecting emotional states were captured and analyzed for each scenario. We implemented multidimensional calibration of emotion-induction materials by: (a) calculating emotion indicators from EEG signals, (b) combining these with traditional assessments using the Self-Assessment Manikin (SAM) scale, and (c) calibrating scenarios for distinct emotion categories. This yields psychophysiologically calibrated VR scenarios for emotion induction, delivering standardised affective stimuli with synchronised EEG datasets to advance affective computing research.

Using mobile phones for photography and videography has become increasingly prevalent. However, novice users often struggle with mastering professional photography techniques and lack the necessary skills for precise camera movement. To address this issue, we propose ARVideoCam, a video shooting guidance method based on augmented reality (AR) designed to assist novice users in accurately controlling camera movements and learning photography skills through imitating high-quality video shooting. ARVideoCam consists of three layers: the extraction layer, the analysis layer, and the AR layer. The extraction layer of the model captures the subject's motion in the sample video, and the analysis layer converts the subject's motion into camera motion, which is then transformed into AR-based guidance in the AR layer. The user study results indicate that AR guidance significantly enhances novice users' ability to imitate sample videos across different types of camera motion, resulting in more efficient and precise camera movements during shooting. This study offers valuable inspiration for the research on AR-assisted video shooting and enhancing video shooting skills among novice users.

We introduce RMAvatar, a novel human avatar representation with Gaussian splatting embedded on mesh to learn clothed avatar from a monocular video. We utilize the explicit mesh geometry to represent motion and shape of a virtual human and implicit appearance rendering with Gaussian Splatting. Our method consists of two main modules: Gaussian initialization module and Gaussian rectification module. We embed Gaussians into triangular faces and control their motion through the mesh, which ensures low-frequency motion and surface deformation of the avatar. Due to the limitations of LBS formula, the human skeleton is hard to control complex non-rigid transformations. We then design a pose-related Gaussian rectification module to learn fine-detailed non-rigid deformations, further improving the realism and expressiveness of the avatar. We conduct extensive experiments on public datasets, and RMAvatar shows state-of-the-art performance on both rendering quality and quantitative evaluations. Please see our project page at https://rm-avatar.github.io .

Virtual reality (VR) mindfulness meditation has emerged as a prominent emotion regulation strategy in recent years. Current research often seeks to enhance meditation effectiveness through biofeedback and overlooks the trajectory of emotional changes and the changing needs during regulation. In this study, we propose an electroencephalography (EEG)–based positive feedback mechanism for VR mindfulness meditation aimed at optimizing the effects of emotion regulation. This mechanism consists of three modules: 1) EEG-based emotional state computation; 2) process-based relaxation assessment; and 3) adaptive positive decision feedback. Collectively, these components form a computation-assessment-feedback closed-loop system that objectively quantifies emotions while enabling real-time decision adjustments based on emotional trends, thereby enhancing user engagement and emotion regulation efficacy through personalized feedback. The contribution of the proposed feedback mechanism was evaluated through a randomized controlled trial (N= 36). The results indicated that both physiological measures and self-reported relaxation significantly increased when compared to interventions without feedback. These findings validate that the EEG-based positive feedback mechanism effectively enhances emotion regulation while providing additional insights into improving both the engagement and effectiveness within digital mental health interventions.

3D skeleton is an inherent structure of objects and is often used for shape analysis. However, most supervised deep learning methods, which directly obtain 3D skeletons from 2D images, are constrained by skeleton data preparation. In this paper, we introduce a self-supervised method S3DS: a differentiable rendering-based method to reconstruct a 3D skeleton of shape from its single-view images, by using medial axis transformation (MAT) as its 3D skeleton. We use medial spheres (center positions and radii) to represent the 3D skeleton and use the connectivity of the spheres (medial mesh) to represent the topology. We trained a medial sphere prediction network, which reconstructs 3D skeleton spheres (centers and radii) from a single-view image and renders them into a 2D silhouette with many circles. Because of the radius, the center of the circle will fall on the 2D skeleton. Then the 3D spheres are fitted to the 3D skeleton by fitting many 2D circles onto the 2D skeleton. A mechanism is proposed to generate the connectivity of the discrete medial spheres and construct the 3D topology of the shape. We have conducted extensive experiments on public datasets and proved that S3DS has better performance than baseline and competitive performances with supervised methods on 3D skeletons reconstruction.

Abstract Virtual pet applications have been widely developed and applied in various fields. Mobile augmented reality (MAR) provides a new medium for virtual pets, allowing users to have a more immersive interactive experience through MAR pets. However, the issue of the user experience in MAR pets remains uninvestigated and relatively unexploited. Therefore, this article proposes a context‐aware MAR pet interaction model (CAPet model) to enhance the user experience in MAR pet systems, which allows the MAR pet makes feedback adaptively corresponding to the dynamic context. In addition, this article presents a user experience pyramid to measure the user experience for MAR pet. According to the proposed CAPet model, a MAR pet‐dog application is designed and implemented, based on which user test are conducted. The results of user test indicate the effectiveness of the proposed method on enhancing user experience, which provides a basis for the design of MAR pet applications in the future.

Abstract In recent years, mindfulness meditation has become increasingly popular as a way to relieve negative emotions. Although many studies have shown the advantages of the most immersive virtual reality (VR) technologies to support mindfulness meditation, few have summarized a standardized process for developing a VR tool to aid mindfulness meditation. We propose an interaction design model for VR mindfulness meditation using imagery‐based transformation and positive feedback to help users quickly soothe their negative emotions. Based on the traditional mindfulness meditation model, we propose the three detailed transformational steps to build (1) an interaction guidance process, (2) an interaction experience context, and (3) an interaction feedback mechanism. We build two scenarios based on them. Our experimental results suggest that our design can effectively alleviate a user's anxiety and provide emotional relief. Our insights on the transformation model of VR mindfulness meditation can be applied in related research, providing an effective reference model for VR mindfulness meditation systems, while providing new ideas for non‐pharmacological interventions in psychotherapy.

Abstract With a core purpose of helping users to understand the context, a water interface provides possibility for enhancing user experience in interaction process. Starting from analyzing existing water‐mediated interaction approaches, we proposed a water‐mediated interaction design model and a corresponding user experience model, aiming to eliminate the boundary between users and the context with water as the medium. According to the proposed model, we implemented a water‐mediated interaction system Landscape Rippling, with the painting “A Panorama of Rivers and Mountains” as its context. Ultimately, user experience tests of the interaction system demonstrate the effectiveness of this water‐mediated interaction design model.

Abstract Extracting a faithful and compact representation of an animated surface mesh is an important problem for computer graphics. However, the surface‐based methods have limited approximation power for volume preservation when the animated sequences are extremely simplified. In this paper, we introduce Deformable Medial Axis Transform (DMAT), which is deformable medial mesh composed of a set of animated spheres. Starting from extracting an accurate and compact representation of a static MAT as the template and partitioning the vertices on the input surface as the correspondences for each medial primitive, we present a correspondence‐based approximation method equipped with an As‐Rigid‐As‐Possible (ARAP) deformation energy defined on medial primitives. As a result, our algorithm produces DMAT with consistent connectivity across the whole sequence, accurately approximating the input animated surfaces.