arXiv subjects

Xian Wang

Publications and source records attributed to Xian Wang.

45 records · Page 3Linked to original sources

arXiv2022

VibroWeight: Simulating Weight and Center of Gravity Changes of Objects in Virtual Reality for Enhanced Realism

Haptic feedback in virtual reality (VR) allows users to perceive the physical properties of virtual objects (e.g., their weight and motion patterns). However, the lack of haptic sensations deteriorates users' immersion and overall experience. In this work, we designed and implemented a low-cost hardware prototype with liquid metal, VibroWeight, which can work in complementarity with commercial VR handheld controllers. VibroWeight is characterized by bimodal feedback cues in VR, driven by adaptive absolute mass (weights) and gravity shift. To our knowledge, liquid metal is used in a VR haptic device for the first time. Our 29 participants show that VibroWeight delivers significantly better VR experiences in realism and comfort.

cs.HC↗

arXiv2021

Real-time Image Enhancer via Learnable Spatial-aware 3D Lookup Tables

Recently, deep learning-based image enhancement algorithms achieved state-of-the-art (SOTA) performance on several publicly available datasets. However, most existing methods fail to meet practical requirements either for visual perception or for computation efficiency, especially for high-resolution images. In this paper, we propose a novel real-time image enhancer via learnable spatial-aware 3-dimentional lookup tables(3D LUTs), which well considers global scenario and local spatial information. Specifically, we introduce a light weight two-head weight predictor that has two outputs. One is a 1D weight vector used for image-level scenario adaptation, the other is a 3D weight map aimed for pixel-wise category fusion. We learn the spatial-aware 3D LUTs and fuse them according to the aforementioned weights in an end-to-end manner. The fused LUT is then used to transform the source image into the target tone in an efficient way. Extensive results show that our model outperforms SOTA image enhancement methods on public datasets both subjectively and objectively, and that our model only takes about 4ms to process a 4K resolution image on one NVIDIA V100 GPU.

cs.CV↗

arXiv2021

Spatial Knowledge Acquisition in Virtual and Physical Reality: A Comparative Evaluation

Virtual Reality (VR) head-mounted displays (HMDs) have been studied widely as tools for the most diverse kinds of training activities. One special kind that is the basis for many real-world applications is spatial knowledge acquisition and navigation. For example, knowing well by heart escape routes can be an important factor for firefighters and soldiers. Prior research on how well knowledge acquired in virtual worlds translates to real, physical one has had mixed results, with some suggesting spatial learning in VR is akin to using a regular 2D display. However, VR HMDs have evolved drastically in the last decade, and little is known about how spatial training skills in a simulated environment using up-to-date VR HMDs compares to training in the real world. In this paper, we aim to investigate how people trained in a VR maze compare against those trained in a physical maze in terms of recall of the position of items inside the environment. While our results did not find significant differences in time performance for people who experienced the physical and those who trained in VR, other behavioural factors were different.

cs.HC↗

arXiv2021

Unexpected Hydrophobicity on Self-Assembled Monolayers Terminated with Two Hydrophilic Hydroxyl Groups

Current major approaches to access surface hydrophobicity include directly introducing hydrophobic nonpolar groups/molecules into surface or elaborately fabricating surface roughness. Here, for the first time, molecular dynamics simulations show an unexpected hydrophobicity with a contact angle of $82^o$ on a flexible self-assembled monolayer terminated only with two hydrophilic OH groups ($(OH)_2\!-\!SAM$). This hydrophobicity is attributed to the formation of a hexagonal-ice-like H-bonding structure in the OH matrix of $(OH)_2\!-\!SAM$, which sharply reduces the hydrogen bonds between surface and water molecules above. The unique simple interface presented here offers a significant molecular-level platform for examining the bio-interfacial interactions ranging from biomolecules binding to cell adhesion.

physics.app-ph↗

arXiv2020

Robust Image Captioning

Automated captioning of photos is a mission that incorporates the difficulties of photo analysis and text generation. One essential feature of captioning is the concept of attention: how to determine what to specify and in which sequence. In this study, we leverage the Object Relation using adversarial robust cut algorithm, that grows upon this method by specifically embedding knowledge about the spatial association between input data through graph representation. Our experimental study represent the promising performance of our proposed method for image captioning.

cs.CV↗

arXiv2020

Evaluating the Effect of Audience in a Virtual Reality Presentation Training Tool

Public speaking is an essential skill in everyone's professional or academic career. Nevertheless, honing this skill is often tricky because training in front of a mirror does not give feedback or inspire the same anxiety as present-ing in front of an audience. Further, most people do not always have access to the place where the presentation will happen. In this research, we developed a Virtual Reality (VR) environment to assist in improving people's presentation skills. Our system uses 3D scanned people to create more realistic scenarios. We conducted a study with twelve participants who had no prior experience with VR. We validated our virtual environment by analyzing whether it was preferred to no VR system and accepted regardless of the existence of a virtual audience. Our results show that users overwhelmingly prefer to use the VR system as a tool to help them improve their public speaking skills than training in an empty environment. However, the preference for an audience is mixed.

cs.HC↗

arXiv2018

Image Super-Resolution Using VDSR-ResNeXt and SRCGAN

Over the past decade, many Super Resolution techniques have been developed using deep learning. Among those, generative adversarial networks (GAN) and very deep convolutional networks (VDSR) have shown promising results in terms of HR image quality and computational speed. In this paper, we propose two approaches based on these two algorithms: VDSR-ResNeXt, which is a deep multi-branch convolutional network inspired by VDSR and ResNeXt; and SRCGAN, which is a conditional GAN that explicitly passes class labels as input to the GAN. The two methods were implemented on common SR benchmark datasets for both quantitative and qualitative assessment.

cs.CV↗

arXiv2017

Probing Laser-induced Microenvironment Changes in Room Temperature Ionic Liquids

Modulating heterogeneous microstructure in room temperature ionic liquids (RTILs) by external stimuli is an important approach for understanding and designing the external field induced chemical reactions in natural and applicable systems. Here, we report for the first time the redistribution of oxygen molecules in RTILs due to laser-induced microstructure changes probed by triplet excited state dynamics of porphyrin and rotational dynamics of coumarin 153. A remarkably long-lived triplet excited state of porphyrin is observed in air-saturated ionic liquid with the changes of microstructure after irradiation, suggesting that more charge-shifted O2 induced by external laser field move into the polar domains of ionic liquid [C8mim][PF6] from nonpolar domains through electrostatic interactions. The results presented here suggest that the heterogeneous systems of ionic liquids upon external stimuli can be designed for those oxygen-related chemical reactions with extensive inspirations for potential applications in lithium-air batteries, gaseous sensing, photoelectrical catalysis and so on.

physics.chem-ph↗

arXiv2014

A Short Image Series Based Scheme for Time Series Digital Image Correlation

A new scheme for digital image correlation, i.e., short time series DIC (STS-DIC) is proposed. Instead of processing the original deformed speckle images individually, STS-DIC combines several adjacent deformed speckle images from a short time series and then processes the averaged image, for which deformation continuity over time is introduced. The deformation of several adjacent images is assumed to be linear in time and a new spatial-temporal displacement representation method with eight unknowns is presented based on the subset-based representation method. Then, the model of STS-DIC is created and a solving scheme is developed based on the Newton-Raphson iteration. The proposed method is verified for numerical and experimental cases. The results show that the proposed STS-DIC greatly improves the accuracy of traditional DIC, both under simple and complicated deformation conditions, while retaining acceptable actual computational cost.

physics.optics↗