Browsing by Author "Geng, Zhenglin"

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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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    Recovering Geometric Information with Learned Texture Perturbations
    (ACM, 2021) Wu, Jane; Jin, Yongxu; Geng, Zhenglin; Zhou, Hui; Fedkiw, Ronald; Narain, Rahul and Neff, Michael and Zordan, Victor
    Regularization is used to avoid overfitting when training a neural network; unfortunately, this reduces the attainable level of detail hindering the ability to capture high-frequency information present in the training data. Even though various approaches may be used to re-introduce high-frequency detail, it typically does not match the training data and is often not time coherent. In the case of network inferred cloth, these sentiments manifest themselves via either a lack of detailed wrinkles or unnaturally appearing and/or time incoherent surrogate wrinkles. Thus, we propose a general strategy whereby high-frequency information is procedurally embedded into low-frequency data so that when the latter is smeared out by the network the former still retains its high-frequency detail. We illustrate this approach by learning texture coordinates which when smeared do not in turn smear out the high-frequency detail in the texture itself but merely smoothly distort it. Notably, we prescribe perturbed texture coordinates that are subsequently used to correct the over-smoothed appearance of inferred cloth, and correcting the appearance from multiple camera views naturally recovers lost geometric information.