Browsing by Author "Zhu, Yilin"

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    A Pixel-Based Framework for Data-Driven Clothing
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Jin, Ning; Zhu, Yilin; Geng, Zhenglin; Fedkiw, Ron; Bender, Jan and Popa, Tiberiu
    We propose a novel approach to learning cloth deformation as a function of body pose, recasting the graph-like triangle mesh data structure into image-based data in order to leverage popular and well-developed convolutional neural networks (CNNs) in a two-dimensional Euclidean domain. Then, a three-dimensional animation of clothing is equivalent to a sequence of twodimensional RGB images driven/choreographed by time dependent joint angles. In order to reduce nonlinearity demands on the neural network, we utilize procedural skinning of the body surface to capture much of the rotation/deformation so that the RGB images only contain textures of displacement offsets from skin to clothing. Notably, we illustrate that our approach does not require accurate unclothed body shapes or robust skinning techniques. Additionally, we discuss how standard image based techniques such as image partitioning for higher resolution can readily be incorporated into our framework.
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    Three Dimensional Reconstruction of Botanical Trees with Simulatable Geometry
    (ACM, 2021) Quigley, Ed; Lin, Winnie; Zhu, Yilin; Fedkiw, Ronald; Narain, Rahul and Neff, Michael and Zordan, Victor
    We tackle the challenging problem of creating full and accurate three dimensional reconstructions of botanical trees with the topological and geometric accuracy required for subsequent physical simulation, e.g. in response to wind forces. Although certain aspects of our approach would benefit from various improvements, our results exceed the state of the art especially in geometric and topological complexity and accuracy. Starting with two dimensional RGB image data acquired from cameras attached to drones, we create point clouds, textured triangle meshes, and a simulatable and skinned cylindrical articulated rigid body model. We discuss the pros and cons of each step of our pipeline, and in order to stimulate future research we make the raw and processed data from every step of the pipeline as well as the final geometric reconstructions publicly available.