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We present a CPU-based real-time cloth animation method for dressing virtual humans of various shapes and poses. Our approach formulates the clothing deformation as a high-dimensional function of body shape parameters and pose parameters. In order to accelerate the computation, our formulation factorizes the clothing deformation into two independent components: the deformation introduced by body pose variation (Clothing Pose Model) and the deformation from body shape variation (Clothing Shape Model). Furthermore, we sample and cluster the poses spanning the entire pose space and use those clusters to efficiently calculate the anchoring points. We also introduce a sensitivity-based distance measurement to both find nearby anchoring points and evaluate their contributions to the final animation. Given a query shape and pose of the virtual agent, we synthesize the resulting clothing deformation by blending the Taylor expansion results of nearby anchoring points. Compared to previous methods, our approach is general and able to add the shape dimension to any clothing pose model. Furthermore, we can animate clothing represented with tens of thousands of vertices at 50+ FPS on a CPU. We also conduct a user evaluation and show that our method can improve a user's perception of dressed virtual agents in an immersive virtual environment (IVE) compared to a realtime linear blend skinning method.

Sharing short personalized videos to various social media networks has become quite popular in recent years. This raises the need for digital retouching of portraits in videos. However, applying portrait image editing directly on portrait video frames cannot generate smooth and stable video sequences. To this end, we present a robust and easy-to-use parametric method to reshape the portrait in a video to produce smooth retouched results. Given an input portrait video, our method consists of two main stages: stabilized face reconstruction, and continuous video reshaping. In the first stage, we start by estimating face rigid pose transformations across video frames. Then we jointly optimize multiple frames to reconstruct an accurate face identity, followed by recovering face expressions over the entire video. In the second stage, we first reshape the reconstructed 3D face using a parametric reshaping model reflecting the weight change of the face, and then utilize the reshaped 3D face to guide the warping of video frames. We develop a novel signed distance function based dense mapping method for the warping between face contours before and after reshaping, resulting in stable warped video frames with minimum distortions. In addition, we use the 3D structure of the face to correct the dense mapping to achieve temporal consistency. We generate the final result by minimizing the background distortion through optimizing a content-aware warping mesh. Extensive experiments show that our method is able to create visually pleasing results by adjusting a simple reshaping parameter, which facilitates portrait video editing for social media and visual effects.

Virtual object manipulation as a key feature has been studied in virtual reality (VR) environments. Previous studies highlighted user experience on three basic types of virtual object manipulation, translation, rotation and scaling. However, prior literature mainly studied task performance in manipulation modes with different degrees of freedom (DoF), and few studies assessed user experience by evaluating the psychological response, such as mental workload on these three basic manipulation types in virtual environments. This paper compared manipulation modes with 1DoF and 3DoF to assess users’ mental workload as a critical indicator of user experience by electroencephalogram (EEG) measurement and questionnaires in manipulation tasks on the webpage with VR effects (also known as WebVR). By applying signal processing and statistical methods to analyze EEG data from ten subjects, the results demonstrated that the participants generally perceive less mental workload by 1DoF manipulation modes than 3DoF on WebVR. Besides, this study also found some different results between objective and subjective data.

Background: Hepatitis B virus X-interacting protein (HBXIP) is associated with a variety of tumors. The purpose of this study was to investigate the clinicopathological significance of HBXIP expression in pancreatic ductal adenocarcinoma (PDAC) and to explore its potential as a biomarker for PDAC. Methods: Immunohistochemical (IHC) staining was performed on 126 PDAC tissues, 36 paraneoplastic tissues and 22 normal pancreatic tissues. The relationship between high levels of HBXIP expression and pathological features of PDAC patients was evaluated by chi-squared values. Results:The positive rate of HBXIP protein in PDAC tissues was 85.7% (108/126), which was significantly higher than that of adjacent pancreatic tissue (41.7%, 15/36) and normal pancreas (18.2%, 4/22). In addition, strong positive expression of HBXIP was associated with tumor size, positive lymph node metastasis, clinical stage and 80-month overall survival. Patient's age, gender, degree of differentiation, Ki-67 expression index, and calcification were, however, not associated with high levels of HBXIP expression. Conclusions:We present association between HBXIP expression and the pathological features of patients with PDAC.

© 2019 Association for Computing Machinery. Modeling and simulation are essential methods to better understand a complex system in the real world. Many simulation systems have strong demand for involving physical devices or equipment in the system to improve the fidelity. But most of the current cloud based simulation systems are designed for equipment with adequate resources and suffering from high latency for their centralized communication. Edge computing is getting much attention as a new paradigm for distributed systems involving heterogeneous devices and requiring real-time communication. However, edge computing is still in its initial stage and there is no consensus on the edge framework and implementation. To the best of our knowledge, no detailed studies have explored the edge-side simulation and modeling. In this paper, we study and design an edge-side simulation framework, which exploits light-weight microservices architecture to build scalable and real-time LVC (Live, Virtual and Constructive) simulations. Specifically, the application model is designed to illustrate how LVC simulation integrates with edge computing and cloud computing. The autonomous negotiation service and time synchronization services are discussed in details. Though it is a preliminary edge computing framework for LVC simulation, no similar research has been conducted for modeling and simulation using edge computing.

Glaciers on the Tibetan Plateau play an important role in the Earth's climate system. Because of high altitude and difficulties in access, regular, systematic surveys of glacier variation are almost impossible in some regions of the Tibetan Plateau. As a result, remote sensing is at present the primary, in many cases the only way to measure the state of glaciers. In combination with Geographic Information System (GIS), it provides an efficient tool to analyze the current state and changes in glaciers. We used a technique of integrated remote sensing and GIS to map and measure changes in glaciers using a 21-year series of Landsat images (from 1973 to 1994) and topographic maps of a study site on the Tibetan Plateau. The details of glacier fluctuation, such as area of glaciers and changes in the magnitude of recession or advance of a glacier terminus on different dates, are described based on the results of image analysis, including coregistration, classification, vector analysis, and area calculation. Within the study area, during the 21 years, it was determined that the northern glaciers advanced and the southern glaciers retreated, the average rate of change was 50 m a−1 to 105 m a−1. The results show that Landsat data provide an important source of information for studies of glacier variation.

Because of the larger trigger lighting area, 220 kV double circuit transmission line is more vulnerable by lightning, and shielding failure and back flashover are more prominent. Therefore, the accurate assessment of the lightning protection performance of 220 kV double circuit transmission line is crucial to ensure the security and stability of the transmission network. This paper presents the main result of a back flashover simulation model for double circuit transmission line using Electromagnetic Transient Program (EMTP), this model include lighting source model, transmission line model, tower impulse current response model, insulator string flashover model, footing impedance model and induced voltage component model. Based on this model, this paper do some simulation to research in the influence that the tower height, footing impedance, soil resistivity and insulation strength have on the lighting protection performance of the 220 kV double circuit transmission line.