As consumer Virtual Reality (VR) and Mixed Reality (MR) technologies gain
momentum, there's a growing focus on the development of engagements with 3D
virtual content. Unfortunately, traditional techniques for content creation,
editing, and interaction within these virtual spaces are fraught with
difficulties. They tend to be not only engineering-intensive but also require
extensive expertise, which adds to the frustration and inefficiency in virtual
object manipulation. Our proposed VR-GS system represents a leap forward in
human-centered 3D content interaction, offering a seamless and intuitive user
experience. By developing a physical dynamics-aware interactive Gaussian
Splatting in a Virtual Reality setting, and constructing a highly efficient
two-level embedding strategy alongside deformable body simulations, VR-GS
ensures real-time execution with highly realistic dynamic responses. The
components of our Virtual Reality system are designed for high efficiency and
effectiveness, starting from detailed scene reconstruction and object
segmentation, advancing through multi-view image in-painting, and extending to
interactive physics-based editing. The system also incorporates real-time
deformation embedding and dynamic shadow casting, ensuring a comprehensive and
engaging virtual experience.Our project page is available at:
this https URL

消费者虚拟现实（VR）和混合现实（MR）技术的发展引发了对与 3D 虚拟内容的互动的关注，然而传统的内容创作、编辑和交互技术存在困难。本研究提出了 VR-GS 系统，通过在虚拟现实环境中开发具有物理动力学感知的交互高斯碎片技术，并结合可变形物体的模拟，以实现无缝和直观的用户体验。该系统通过详细的场景重建和物体分割、多视角图像修复，以及基于物理的交互编辑，同时包含实时变形嵌入和动态阴影投射等功能，确保了全面且引人入胜的虚拟体验。

VR-GS：一种虚拟现实中的物理动态感知互动高斯喷洒系统

VR-GS: A Physical Dynamics-Aware Interactive Gaussian Splatting System  in Virtual Reality

Misinformation spread in online social networks is an urgent-to-solve problem
having harmful consequences that threaten human health, public safety,
economics, and so on. In this study, we construct a novel dataset, called
MiDe-22, having 5,284 English and 5,064 Turkish tweets with their
misinformation labels under several recent events, including the Russia-Ukraine
war, COVID-19 pandemic, and Refugees. Moreover, we provide the user engagements
to the tweets in terms of likes, replies, retweets, and quotes. We present a
detailed data analysis with descriptive statistics and temporal analysis, and
provide the experimental results of a benchmark evaluation for misinformation
detection on our novel dataset.

本研究构建了一个新的数据集 MiDe-22，包括 5,284 条英文推文和 5,064 条土耳其推文，涵盖了包括俄罗斯 - 乌克兰战争、COVID-19 疫情和难民等几个最近事件，并提供了推文的用户参与度。研究提供了详细的数据分析和实验结果，并对误信息检测进行了基准评估。