3D multi-object arrangement is a fundamental task in VR that relies on accurate and natural initial selection alongside rapid and convenient subsequent manipulation to ensure high efficiency. However, existing methods fail to support efficient multi-object arrangement in highly occluded scenes with densely packed candidate objects through controller-free natural interactions. In this article, we propose an efficient, scene-aware multi-object arrangement method (MOA) designed for fast, precise, and convenient object arrangement. First, MOA introduces an importance-driven multi-object initial selection algorithm that assigns higher spatiotemporally correlated object importance (IMP) to target objects, establishing a natural multi-object initial selection mode that enables quick and accurate selection of high-IMP objects. Subsequently, it presents an auxiliary-structure-guided multi-object manipulation algorithm that constructs an auxiliary manipulation structure to assist subsequent multi-object manipulation, alongside a multi-modal interaction mode that facilitates swift and natural manipulation. Compared to state-of-the-art controller-free and controller-based methods, MOA significantly improves task performance, reduces task load, and enhances convenience in complex multi-object arrangement scenes involving hundreds of highly occluded objects need to be arranged.

Cybersickness significantly impairs user comfort and immersion in virtual reality (VR). Effective identification of cybersickness leveraging physiological, visual, and motion data is a critical prerequisite for its mitigation. However, current methods primarily employ direct feature fusion across modalities, which often leads to limited accuracy due to inadequate modeling of inter-modal relationships. In this paper, we propose a multimodal contrastive learning method for cybersickness recognition. First, we introduce Brain Connectivity Graph Representation (BCGR), an innovative graph-based representation that captures cybersickness-related connectivity patterns across modalities. We further develop three BCGR instances: E-BCGR, constructed based on EEG signals; MV-BCGR, constructed based on video and motion data; and S-BCGR, obtained through our proposed standardized decomposition algorithm. Then, we propose a connectivity-constrained contrastive fusion module, which aligns E-BCGR and MV-BCGR into a shared latent space via graph contrastive learning while utilizing S-BCGR as a connectivity constraint to enhance representation quality. Moreover, we construct a multimodal cybersickness dataset comprising synchronized EEG, video, and motion data collected in VR environments to promote further research in this domain. Experimental results demonstrate that our method outperforms existing state-of-the-art methods across four critical evaluation metrics: accuracy, sensitivity, specificity, and the area under the curve. Source code: https://github.com/PEKEW/cybersickness-bcgr.

Text entry poses a significant challenge in the realm of virtual reality (VR). This paper introduces FanPad, a novel solution designed to facilitate dual-hand text input within head-mounted displays (HMDs). FanPad accomplishes this by ingeniously mapping and curving the 26 typing keys (T26) QWERTY keyboard onto the touchpads of both controllers. The curved key layout of FanPad is derived from the natural movement of the thumb when interacting with the touchpad, resembling an arc with a thumb-length fixed radius.To optimize the experience, we introduce a customization process for the FanPad curve to better cope with individual hand shapes and thumb movements. We also provide a version with more overlap area named FanPad-Ov for different users with different typing habits.Our first user study examined the effects of curving and different overlap areas by comparing four potential layouts. The results clearly favor the FanPad and FanPad-Ov layout compared to the nocurving version, SKPad(-Ov). Subsequently, the second user study was conducted to assess long-term performance and improvement on customized FanPads. Notably, novices achieved a typing speed of 19.73 words per minute (WPM), demonstrating a remarkable increase of 58.47% after a 60-phrase training in six days. The highest typing speed reached an impressive 24.19 WPM.