30 research outputs found
A GPU-based Streaming Algorithm for High-Resolution Cloth Simulation
No full textWe present a GPU-based streaming algorithm to perform high-resolution and accurate cloth simulation. We map all the components of cloth simulation pipeline, including time integration, collision detection, collision response, and velocity updating to GPU-based kernels and data structures. Our algorithm perform intra-object and inter-object collisions, handles contacts and friction, and is able to accurately simulate folds and wrinkles. We describe the streaming pipeline and address many issues in terms of obtaining high throughput on many-core GPUs. In practice, our algorithm can perform high-fidelity simulation on a cloth mesh with 2M triangles using 3GB of GPU memory. We highlight the parallel performance of our algorithm on three different generations of GPUs. On a high-end NVIDIA Tesla K20c, we observe up to two orders of magnitude performance improvement as compared to a single-threaded CPU-based algorithm, and about one order of magnitude improvement over a 16-core CPU-based parallel implementation.Computer Graphics Foru
DFGA: Digital Human Faces Generation and Animation from the RGB Video using Modern Deep Learning Technology
No full textHigh-quality and personalized digital human faces have been widely used in media and entertainment, from film and game production to virtual reality. However, the existing technology of generating digital faces requires extremely intensive labor, which prevents the large-scale popularization of digital face technology. In order to tackle this problem, the proposed research will investigate deep learning-based facial modeling and animation technologies to 1) create personalized face geometry from a single image, including the recognizable neutral face shape and believable personalized blendshapes; (2) generate personalized production-level facial skin textures from a video or image sequence; (3) automatically drive and animate a 3D target avatar by an actor's 2D facial video or audio. Our innovation is to achieve these tasks both efficiently and precisely by using the end-to-end framework with modern deep learning technology (StyleGAN, Transformer, NeRF).Pacific Graphics Short Papers, Posters, and Work-in-Progress PapersDigital Huma
Global Stability of a Markovian Jumping Chaotic Financial System with Partially Unknown Transition Rates under Impulsive Control Involved in the Positive Interest Rate
No full textThe intrinsic instability of the financial system itself results in chaos and unpredictable economic behavior. To gain the globally asymptotic stability of the equilibrium point with a positive interest rate of the chaotic financial system, pulse control is sometimes very necessary and is employed in this paper to derive the globally exponential stability of financial system. It should be pointed out that the delayed feedback model brings an essential difficulty so that the regional control method has to be adopted. In this paper, the author firstly employs impulsive control, regional control, the Lyapunov function technique, and variational methods to derive the stochastically globally asymptotic stability criterion of the economic balance point with a positive interest rate for a delayed feedback financial system with Markovian jumping and partially unknown transition rates. Besides, the mathematical induction method and the proof by contradiction are applied synthetically to deduce the globally exponential stability of the equilibrium point with a positive interest rate for the impulsive financial system without time-delays. Moreover, numerical examples illustrate that under suitable data conditions on the two main criteria mentioned above, the interest rates are positive decimals when the financial system reaches stability, which means better economic significance
The Application of 4C Training Model in the Operating Room Nurse Training
No full textThe operating room is an important place for the treatment of patients undergoing surgery, the quality level of clinical nursing has a direct influence in the therapeutic effect of patients. In the present study, four components instructional design mode (4C training mode) was applied in the training of professional nurses in the operating room, it involved the following aspects: regular skill knowledge analysis, goals setting, as well as the construction and implementation of the project. Targeted training methods and training content were formulated in accordance with the learning ability and knowledge reserve of different professional nurses, and different skills required to master, to fully mobilize the enthusiasm and initiative of nurses, thereby to improve the operation and comprehensive level of nurses in the operation room to achieve good results
Development and validation of an LC–MS/MS method for the determination of hinokiflavone in rat plasma and its application to a pharmacokinetic study
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B-spine: Learning B-spline Curve Representation for Robust and Interpretable Spinal Curvature Estimation
No full textSpinal curvature estimation is important to the diagnosis and treatment of the scoliosis. Existing methods face several issues such as the need of expensive annotations on the vertebral landmarks and being sensitive to the image quality. It is challenging to achieve robust estimation and obtain interpretable results, especially for low-quality images which are blurry and hazy. In this paper, we propose B-Spine, a novel deep learning pipeline to learn B-spline curve representation of the spine and estimate the Cobb angles for spinal curvature estimation from low-quality X-ray images. Given a low quality input, a novel SegRefine network which employs the unpaired image-to-image translation is proposed to generate a high quality spine mask from the initial segmentation result. Next, a novel mask-based B-spline prediction model is proposed to predict the B-spline curve for the spine centerline. Finally, the Cobb angles are estimated by a hybrid approach which combines the curve slope analysis and a curve based regression model. We conduct quantitative and qualitative comparisons with the representative and SOTA learning-based methods on the public AASCE2019 dataset and our new proposed JLU-CJUH dataset which contains more challenging low-quality images. The superior performance on both datasets shows our method can achieve both robustness and interpretability for spinal curvature estimation
