Browsing by Author "You, Lihua"

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    DFGA: Digital Human Faces Generation and Animation from the RGB Video using Modern Deep Learning Technology
    (The Eurographics Association, 2022) Jiang, Diqiong; You, Lihua; Chang, Jian; Tong, Ruofeng; Yang, Yin; Parakkat, Amal D.; Deng, Bailin; Noh, Seung-Tak
    High-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).
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    Monte Carlo Vortical Smoothed Particle Hydrodynamics for Simulating Turbulent Flows
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Ye, Xingyu; Wang, Xiaokun; Xu, Yanrui; Kosinka, Jiri; Telea, Alexandru C.; You, Lihua; Zhang, Jian Jun; Chang, Jian; Bermano, Amit H.; Kalogerakis, Evangelos
    For vortex particle methods relying on SPH-based simulations, the direct approach of iterating all fluid particles to capture velocity from vorticity can lead to a significant computational overhead during the Biot-Savart summation process. To address this challenge, we present a Monte Carlo vortical smoothed particle hydrodynamics (MCVSPH) method for efficiently simulating turbulent flows within an SPH framework. Our approach harnesses a Monte Carlo estimator and operates exclusively within a pre-sampled particle subset, thus eliminating the need for costly global iterations over all fluid particles. Our algorithm is decoupled from various projection loops which enforce incompressibility, independently handles the recovery of turbulent details, and seamlessly integrates with state-of-the-art SPH-based incompressibility solvers. Our approach rectifies the velocity of all fluid particles based on vorticity loss to respect the evolution of vorticity, effectively enforcing vortex motions. We demonstrate, by several experiments, that our MCVSPH method effectively preserves vorticity and creates visually prominent vortical motions.