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Can i Get There? Negotiated User-to-User Teleportations in Social VR

IEEE Transactions on Visualization and Computer Graphics 2025年第5期31卷 2320-2330页

作者： Wang, Miao Shu, Wen-Tong Li, Yi-Jun Li, Wanwan School of Computer Science and Engineering State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China Zhongguancun Laboratory Beijing100094 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Tandy School of Computer Science University of Tulsa Tulsa United States

The growing adoption of social virtual reality (VR) platforms underscores the importance of safeguarding personal VR space to maintain user privacy and security. Teleportation, a prevalent instantaneous locomotion method in VR, facilitates user engagement but can also inadvertently intrude upon personal VR space, thereby raising privacy concerns. This paper introduces three innovative negotiated teleportation techniques designed to secure user-to-user teleportation and protect personal space privacy, all under a unified small-group development framework. We have designed and evaluated three types of negotiated teleportation techniques: Sector technique for directional control, Distance technique for minimum social distance control, and Area technique for defining circular permissible teleportation areas. These techniques foster a collaborative approach to selecting teleportation points that respect personal space. To evaluate the efficacy of these techniques, we conducted a user study with 20 participants who performed social tasks within a virtual campus environment. The findings demonstrate that our techniques significantly enhance privacy protection and alleviate anxiety associated with unwanted proximity in social VR. © 1995-2012 IEEE.

关键词： virtual environments

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Expression Fusion to Enhance Video and Speech-Driven 3D Facial Animation 41st

Expression Fusion to Enhance Video and Speech-Driven 3D Fa...

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41st Computer Graphics International Conference, CGI 2024

作者： Liu, Yangyue Hu, Yong Shen, Xukun State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China School of New Media Art and Design Beihang University Beijing100191 China

ISBN: (纸本)9783031820205

Currently, monocular 3D face capture and tracking methods are difficult to ensure the accuracy of identity and expression, and speech-driven 3D facial animation methods are challenging to acquire head pose and upper facial expression. In response, we propose a video- and speech-driven 3D facial animation synthesis method that attempts to combine the benefits of these methods and avoid their weaknesses. Specifically, to facilitate animation and handle the problem of character identity accuracy, we generate character appearance templates by registering a 3D morphable model (3DMM) to a rigid model. To address the limitations of different methods for acquiring expressions and poses, we design an expression fusion network based on the 3DMM space to fuse the expression data acquired by different modalities and output unified facial expression data. Finally, due to the limitation of the dataset ignoring the eye movements, we design an eye movement enhancement network to add eyelid movements by modifying the facial expression data and then replacing the eye region to get the final 3D face mesh animation. Through detailed experiments, we demonstrate that our method can generate speech-visual synchronized 3D face animations while obtaining better performance results than the current concerns of using monocular video images or speech-driven generation of 3D face animation methods independently. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Animation

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RDM: A Light Field Super-Resolution Model Improved with Retinex Decomposition 16

RDM: A Light Field Super-Resolution Model Improved with Reti...

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16th International Conference on Signal Processing systems, ICSPS 2024

作者： Lyu, Wenqi Ke, Wei Sheng, Hao Ma, Xiao Liu, Lixue Faculty of Applied Sciences Macao Polytechnic University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China

ISBN: (纸本)9781510689251

Light field cameras hold significant value in applications such as depth estimation, 3D video acquisition, and image super-resolution. Compared to traditional single-image super-resolution methods, light field images can capture object information from multiple spatial and angular perspectives, which is more conducive to capturing detail information. However, current advanced light field super-resolution models still exhibit issues like blurriness, jaggedness, and dimness in 4x light field super-resolution. To address this, we propose a novel light field super-resolution framework that utilizes the Retinex decomposition concept to solve common problems such as ghosting and overlap, thereby improving image quality. By decomposing the light field image into illumination and reflection components and processing them separately, our model significantly enhances detail preservation and color consistency. Experimental results on multiple datasets demonstrate that our model exhibits outstanding performance, both subjectively and objectively, highlighting the effectiveness of this framework and its broad application potential in the field of computational imaging. © 2025 SPIE.

关键词： Image acquisition

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Segmentation of CAD models using hybrid representation

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虚拟现实与智能硬件(中英文) 2025年第2期7卷 188-202页

作者： Claude UWIMANA Shengdi ZHOU Limei YANG Zhuqing LI Norbelt MUTAGISHA Edouard NIYONGABO Bin ZHOU State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Peace-Bay Academy of Virtual Reality Shenyang 110004China China Aerospace Systems Engineering Corporation Beijing 100070China Department of Signal and Information Processing Beihang UniversityBeijing 100191China Department of Computer Science University of SherbrookeSherbrooke J1C0A1Canada

In this paper,we introduce an innovative method for computer-aided design(CAD)segmentation by concatenating meshes and CAD *** previous CAD segmentation methods have achieved impressive performance using single representations,such as meshes,CAD,and point ***,existing methods cannot effectively combine different three-dimensional model types for the direct conversion,alignment,and integrity maintenance of geometric and topological ***,we propose an integration approach that combines the geometric accuracy of CAD data with the flexibility of mesh representations,as well as introduce a unique hybrid representation that combines CAD and mesh models to enhance segmentation *** combine these two model types,our hybrid system utilizes advanced-neural-network techniques to convert CAD models into mesh *** complex CAD models,model segmentation is crucial for model retrieval and *** partial retrieval,it aims to segment a complex CAD model into several simple *** first component of our hybrid system involves advanced mesh-labeling algorithms that harness the digitization of CAD properties to mesh *** second component integrates labelled face features for CAD segmentation by leveraging the abundant multisemantic information embedded in CAD *** combination of mesh and CAD not only refines the accuracy of boundary delineation but also provides a comprehensive understanding of the underlying object *** study uses the Fusion 360 Gallery *** results indicate that our hybrid method can segment these models with higher accuracy than other methods that use single representations.

关键词： B-RepNet hybrid segmentation CAD models classification MeshCNN MeshCAD-Net

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Detail Enhancement for Free Surface LBM Using Adaptive Sizing of Coupled Particles 1st

Detail Enhancement for Free Surface LBM Using Adaptive Sizi...

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1st International Conference on Extended reality, ICXR 2024

作者： Qu, Qingyue Liu, Huiwen Zhu, Shaonan Hao, Aimin Yu, Peng Gao, Yang State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science Yangtze Power Co. Ltd. Hubei Yichang443000 China

ISBN: (纸本)9789819636785

Physically based fluid simulation plays an important role in the immersion and realistic of VR and AR applications. However, efficiency is the bottleneck problem that restricts physical applications. Among those physics-related fluid models, the Lattice Boltzmann Method (LBM), with its localized algorithm, offers high efficiency. However, as a purely Eulerian grid-based approach, achieving rich detail necessitates high resolution. Conversely, Lagrange-based methods can capture intricate details but require substantial computational time to solve large-scale linear systems and manage numerous particle operations. To address these challenges, we propose a hybrid solver that couples the efficient LBM solver with an adaptive particle interpolation method for detail enhancement. Our innovative adaptive particle strategy further reduces particle count while preserving surface detail and improving temporal efficiency by dynamically adjusting particle size based on particle position, distance, and velocity. Experiments demonstrate that our method can display more surface details than LBM alone, delivering visual effects comparable to direct particle interpolation but with higher efficiency. Moreover, our method yields superior visual results compared to adaptive particle methods based on the FLIP approach. This approach is particularly well-suited for large-scale simulations, enhancing both visual quality and time efficiency, and shows great potential for real-time fluid simulations in VR applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Computational fluid dynamics

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SIE-DepthNet: Semantic-Guided Monocular Depth Estimation for Dynamic Environment 1st

SIE-DepthNet: Semantic-Guided Monocular Depth Estimation for...

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1st International Conference on Extended reality, ICXR 2024

作者： Song, Zilong Gao, Yang Dai, Sijia Li, Shuai Hao, Aimin Zhang, Shoulong State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China China Nuclear Power Engineering Co. Ltd. Hebei Branch Ningde China Zhongguancun Laboratory Beijing China

ISBN: (纸本)9789819636785

Depth estimation is a fundamental aspect of spatial computation in virtual-real fusion contexts, wherein self-supervised monocular depth estimation presents a persistent challenge within the fields of computer vision and augmented/mixed reality (AR/XR) applications. However, existing methods face difficulties with moving objects, occlusions, and motion blur, resulting in inaccurate estimates, particularly in dynamic regions. Previous solutions either omit challenging areas in the training process or employ pseudo-depth labels, which still cannot fully address the issues. In this paper, we introduce our semantically implicit and explicit depth estimation network, SIE-DepthNet, a novel approach that leverages semantic information to improve monocular depth estimation in dynamic scenes. We articulate a Depth Semantic Feature Fusion Network (DSFFNet) to provide implicit guidance, ensuring consistent depth distributions across categories. Additionally, we propose a semantic-guided ranking loss that optimizes depth accuracy by aligning estimated values with segmentation boundaries, using uncertainty-aware weighting to mitigate the impact of segmentation noise. Extensive experimental results demonstrate that SIE-DepthNet achieves accurate depth map predictions, significantly enhancing performance in dynamic and static scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Attention mechanism Monocular depth estimation Semantic-guided self-supervised depth estimation

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JGHand: Joint-Driven Animatable Hand Avater via 3D Gaussian Splatting

arXiv

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arXiv 2025年

作者： Sun, Zhoutao Shen, Xukun Hu, Yong Zhong, Yuyou Zhou, Xueyang State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China

Since hands are the primary interface in daily interactions, modeling high-quality digital human hands and rendering realistic images is a critical research problem. Furthermore, considering the requirements of interactive and rendering applications, it is essential to achieve real-time rendering and driveability of the digital model without compromising rendering quality. Thus, we propose Jointly 3D Gaussian Hand (JGHand), a novel joint-driven 3D Gaussian Splatting (3DGS)-based hand representation that renders high-fidelity hand images in real-time for various poses and characters. Distinct from existing articulated neural rendering techniques, we introduce a differentiable process for spatial transformations based on 3D key points. This process supports deformations from the canonical template to a mesh with arbitrary bone lengths and poses. Additionally, we propose a real-time shadow simulation method based on per-pixel depth to simulate self-occlusion shadows caused by finger movements. Finally, we embed the hand prior and propose an animatable 3DGS representation of the hand driven solely by 3D key points. We validate the effectiveness of each component of our approach through comprehensive ablation studies. Experimental results on public datasets demonstrate that JGHand achieves real-time rendering speeds with enhanced quality, surpassing state-of-the-art methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Digital elevation model

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LipText: Lip Tracking Based Text Entry in VR 1st

LipText: Lip Tracking Based Text Entry in VR

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1st International Conference on Extended reality, ICXR 2024

作者： Leng, Jiaye Wang, Zijun Wu, Jian Wang, Lili School of Creative Media City University of Hong Kong Kowloon Tong Hong Kong State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China

ISBN: (纸本)9789819636785

Text entry is an important task in virtual reality (VR), and most existing methods require hand involvement, while hands-free typing has great potential for applications in mobile scenarios. Existing hands-free text entry methods are usually implemented by combining the head and eyes with techniques such as Dwell, Blink and Gesture, which can easily fatigue the user. In this paper, we propose LipText, a lip-tracking-based text entry method in VR. We use a neural network to perform letter-level prediction on the lip data captured by the facial tracker and use head-based selection as an auxiliary to improve the accuracy. We conduct a user study to evaluate our method, the results show a typing speed of 8.63 WPM for the novice group, 9.81 WPM for the potential expert group, and the highest recorded typing speed is 11.13 WPM achieved by a potential expert. Our method is also novice-friendly, and their typing speed increased by 64.38% over a six-day practice. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： virtual environments

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Cluster-Detection-Based Local Super-Resolution Network for Remote Sensing Image Enhancement 1st

Cluster-Detection-Based Local Super-Resolution Network for ...

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1st International Conference on Extended reality, ICXR 2024

作者： Li, Lu Zhu, Xia Ni, Shaofeng Gao, Fan Cao, Dinglun Pei, Ziyi Li, Shuai State Grid Location based Service Co. Ltd Beijing China State Grid Information & Telecommunication Group Co. Ltd. Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China

ISBN: (纸本)9789819636785

Remote sensing images are essential for various applications such as environmental monitoring and urban planning. However, the spatial resolution of these images often hinders the accurate detection of small-scale or densely packed targets. In this work, we advocate a novel method that introduces clustering into local super-resolution networks to enhance image resolution and improve the detectability of small-scale and dense regions. First, K-means clustering is applied to identify dense areas based on both the color and spatial properties of image pixels, efficiently pinpointing regions where small objects are concentrated. Next, a lightweight cross-attention-based super-resolution network is employed to enhance the resolution of these key regions, which improves object detection accuracy. The proposed method is computationally efficient, incorporating depthwise separable convolutions and focusing the cross-attention mechanism on regions of interest, thereby minimizing overhead. Extensive experiments on the WV-3 and DOTA datasets demonstrate that our method significantly improves image quality, outperforming existing methods in terms of both PSNR and SSIM. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optical remote sensing

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Text-driven 3D Human Generation via Contrastive Preference Optimization

arXiv

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arXiv 2025年

作者： Zhou, Pengfei Shen, Xukun Hu, Yong State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China

Recent advancements in Score Distillation Sampling (SDS) have significantly improved text-driven 3D human generation, enabling plausible synthesis from natural language descriptions. However, existing methods struggle with fine-grained semantic alignment, leading to inconsistencies in structure, texture, and attribute representation. To address these challenges, we propose a contrastive preference modeling framework that integrates both positive and negative prompts to enhance SDS. This approach refines text-conditioned generation, improving semantic consistency and 3D fidelity. Specifically, we introduce a preference optimization module that aggregates knowledge from multiple preference models to ensure accurate representation of intricate attributes and nuanced details. Additionally, we propose a negation preference module with static-dynamic negation prompts to systematically distinguish relevant details from extraneous elements, ensuring geometric and textural consistency. To further mitigate artifacts and prevent reward hacking, we develop an LLM-driven dynamic negation prompting mechanism that analyzes textual inputs, decomposes complex attributes, and filters irrelevant components. Extensive experiments demonstrate that our method achieves state-of-the-art performance, significantly enhancing texture realism, structure coherence, and overall visual alignment, particularly for complex and lengthy textual descriptions. © 2025, CC BY.

关键词： Three dimensional computer graphics

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