Motion sickness, in addition to its persistent long-term effects, also exhibits short-term effects characterized as transient physiological discomfort, which changes rapidly with variations in locomotion. However, such discomforts are challenging to assess using current subjective scales and objective physiological measurements. To tackle this issue, this paper suggests continuous measurement methods designed specifically for evaluating transient physiological discomfort during VR locomotion. Through a user-elicitation study, three preferred measurement methods—’squeezing ball’, ’sliding thumb’, and ’rubbing thigh’—were identified. These techniques were then evaluated for reliability, validity, attention, presence, and workload, with ’sliding thumb’ identified as the most effective option. The paper expands traditional measurement methods to capture users’ physiological experiences in VR interactions, offering practical choices for researchers in this field along with an in-depth discussion of design considerations, detailed implementation guidelines, and potential ways to optimize the VR experiences utilizing the measurement data.

This paper presents an N-ary Gaussian Model for predicting endpoint distributions in pointing tasks across task scenarios. Built on the foundational principles of the Ternary Gaussian model series, our model framework allows researchers to define parameter constraints and automatically refine model combinations, eliminating the need for predefined equations based on data analysis. We utilize the Bayesian Information Criterion (BIC) for model selection, ensuring simplicity while maintaining predictive accuracy. We conducted a comparative analysis against published baselines across 7 diverse datasets, covering 1D, 2D, and 3D tasks, different input modalities, different display devices, and time-constrained scenarios, demonstrating the robustness and generalization of the N-ary Gaussian Model. The N-ary Gaussion model offers an automated solution for modeling pointing uncertainty, and also incorporates cross output device, input modality, and temporal constraint factors into spatial pointing uncertainty modeling for the first time.

Immersive creation tools enhance spatial understanding, yet they push animators to abandon mature 2D desktop practices; Pure desktop setups, however, lack embodied spatial manipulation, making tool switching cognitively costly. To address this issue, We present AniXDim, a cross-dimensional interaction system for 3D character animation that unifies precise planar editing and embodied spatial control without explicit mode toggles. A commodity mouse is augmented for on-demand 6DoF manipulation, and a VR controller is endowed with high-precision planar input through adaptive projection, forming bidirectional dimensional augmentation. Users simply raise or rest the device to enable near-frictionless transitions, while a single autostereoscopic desktop display adapts depth to the inferred interaction state, supporting keyframe, pose, and reference authoring within one consistent workspace. A controlled user study comparing AniXDim with pure desktop and pure VR baselines indicates significant performance gains over the desktop baseline and comparable efficiency to the VR baseline, and higher usability ratings while maintaining comparable workload. These findings suggest that near-frictionless dimensional switching can reconcile 2D precision and 3D embodiment, offering a low-learning-overhead pathway for future hybrid desktop animation pipelines and broader 2D–3D authoring domains.

Reminiscence has been shown to provide benefits for older adults, but traditionally relies on personal photos as memory cues and interactions with real people who may not always be available. We present ReminiBuddy, a novel LLM-powered multi-agent conversational system, which allows older adults to engage with two distinct agents - one embodying an older identity and the other a younger identity - while using not only personal photos but also 3D models of generic nostalgic objects as memory cues. Our study, with older adult participants, found that the conversational approach both enjoyable and beneficial for reminiscence. While the younger agent was perceived as more emotionally engaging, the older one fostered greater resonance in content. Personal photos prompted autobiographical memories, whereas 3D generic nostalgic objects evoked shared memories of an era, contributing to a more multifaceted reminiscence experience. We further present design implications for better supporting older adults in reminiscing with LLM-powered conversational agents.

Mixed reality platforms allow users to create virtual environments, yet novice users struggle with both ideation and execution in spatial design. While existing AI models can automatically generate scenes based on user prompts, the lack of interactive control limits users' ability to iteratively steer the output. In this paper, we present EchoLadder, a novel human-AI collaboration pipeline that leverages large vision-language model (LVLM) to support interactive scene modification in virtual reality. EchoLadder accepts users' verbal instructions at varied levels of abstraction and spatial specificity, generates concrete design suggestions throughout a progressive design process. The suggestions can be automatically applied, regenerated and retracted by users' toggle control.Our ablation study showed effectiveness of our pipeline components. Our user study found that, compared to baseline without showing suggestions, EchoLadder better supports user creativity in spatial design. It also contributes insights on users' progressive design strategies under AI assistance, providing design implications for future systems.

This work presents Dual-Stick, a novel controller with two sticks connected at the end that innovates a Dual-Ray interaction paradigm to enrich raycasting input in Virtual Reality (VR). Dual-Stick leverages the inherent human dexterity in using everyday tools such as clamps and tweezers to adjust the relative angle between two sticks. This design supports Dual-Ray interactions that provide with a heuristics-based enhanced mechanism. It also offers more flexible manipulation by taking advantages of additional degrees of freedom provided by clamping angle. We conducted two studies to evaluate the effectiveness of Dual-Ray in target selection and manipulation tasks. The results indicated that Dual-Ray significantly improved efficiency in target selection compared to single-ray input but did not outperform the enhanced single-ray technique. In terms of manipulation, Dual-Ray effectively reduced completion time and mode switching compared to single-ray input.

Interacting with out of reach or occluded VR objects can be cumbersome. Although users can change their position and orientation, such as via teleporting, to help observe and select, doing so frequently may cause loss of spatial orientation or motion sickness. We present vMirror, an interactive widget leveraging reflection of mirrors to observe and select distant or occluded objects. We first designed interaction techniques for placing mirrors and interacting with objects through mirrors. We then conducted a formative study to explore a semi-automated mirror placement method with manual adjustments. Next, we conducted a target-selection experiment to measure the effect of the mirror’s orientation on users’ performance. Results showed that vMirror can be as efficient as direct target selection for most mirror orientations. We further compared vMirror with teleport technique in a virtual treasure hunt game and measured participants’ task performance and subjective experiences. Finally, we discuss vMirorr user experience and present future directions.

The use of Virtual Reality (VR) in applications such as data analysis, artistic creation, and clinical settings requires high precision input. However, the current design of handheld controllers, where wrist rotation is the primary input approach, does not exploit the human fingers' capability for dexterous movements for high precision pointing and selection. To address this issue, we investigated the characteristics and potential of using a pen as a VR input device. We conducted two studies. The first examined which pen grip allowed the largest range of motion---we found a tripod grip at the rear end of the shaft met this criterion. The second study investigated target selection via 'poking' and ray-casting, where we found the pen grip outperformed the traditional wrist-based input in both cases. Finally, we demonstrate potential applications enabled by VR pen input and grip postures.

Haptic simulation of hand tools like wrenches, pliers, scissors and syringes are beneficial for finely detailed skill training in VR, but designing for numerous hand tools usually requires an expert-level knowledge of specific mechanism and protocol. This paper presents HapLinkage, a prototyping framework based on linkage mechanism, that provides typical motion templates and haptic renderers to facilitate proxy design of virtual hand tools. The mechanical structures can be easily modified, for example, to scale the size, or to change the range of motion by selectively changing linkage lengths. Resistant, stop, release, and restoration force feedback are generated by an actuating module as part of the structure. Additional vibration feedback can be generated with a linear actuator. HapLinkage enables easy and quick prototypting of hand tools for diverse VR scenarios, that embody both of their kinetic and haptic properties. Based on interviews with expert designers, it was confirmed that HapLinkage is expressive in designing haptic proxy of hand tools to enhance VR experiences. It also identified potentials and future development of the framework.

Understanding the selection uncertainty of moving targets is a fundamental research problem in HCI. However, the only few works in this domain mainly focus on selecting 1D moving targets with certain input devices, where the model generalizability has not been extensively investigated. In this paper, we propose a 2D Ternary-Gaussian model to describe the selection uncertainty manifested in endpoint distribution for moving target selection. We explore and compare two candidate methods to generalize the problem space from 1D to 2D tasks, and evaluate their performances with three input modalities including mouse, stylus, and finger touch. By applying the proposed model in assisting target selection, we achieved up to 4% improvement in pointing speed and 41% in pointing accuracy compared with two state-of-the-art selection technologies. In addition, when we tested our model to predict pointing errors in a realistic user interface, we observed high fit of 0.94 R2.