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.

Existing deep learning-based surrogate models facilitate efficient data generation, but fall short in uncertainty quantification, efficient parameter space exploration, and reverse prediction. In our work, we introduce SurroFlow, a novel normalizing flow-based surrogate model, to learn the invertible transformation between simulation parameters and simulation outputs. The model not only allows accurate predictions of simulation outcomes for a given simulation parameter but also supports uncertainty quantification in the data generation process. Additionally, it enables efficient simulation parameter recommendation and exploration. We integrate SurroFlow and a genetic algorithm as the backend of a visual interface to support effective user-guided ensemble simulation exploration and visualization. Our framework significantly reduces the computational costs while enhancing the reliability and exploration capabilities of scientific surrogate models.

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.