Browsing by Author "Zou, Changqing"

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    PencilArt: A Chromatic Penciling Style Generation Framework
    (© 2018 The Eurographics Association and John Wiley & Sons Ltd., 2018) Gao, Chengying; Tang, Mengyue; Liang, Xiangguo; Su, Zhuo; Zou, Changqing; Chen, Min and Benes, Bedrich
    Non‐photorealistic rendering has been an active area of research for decades whereas few of them concentrate on rendering chromatic penciling style. In this paper, we present a framework named as PencilArt for the chromatic penciling style generation from wild photographs. The structural outline and textured map for composing the chromatic pencil drawing are generated, respectively. First, we take advantage of deep neural network to produce the structural outline with proper intensity variation and conciseness. Next, for the textured map, we follow the painting process of artists to adjust the tone of input images to match the luminance histogram and pencil textures of real drawings. Eventually, we evaluate PencilArt via a series of comparisons to previous work, showing that our results better capture the main features of real chromatic pencil drawings and have an improved visual appearance.Non‐photorealistic rendering has been an active area of research for decades whereas few of them concentrate on rendering chromatic penciling style. In this paper, we present a framework named as PencilArt for the chromatic penciling style generation from wild photographs. The structural outline and textured map for composing the chromatic pencil drawing are generated, respectively. First, we take advantage of deep neural network to produce the structural outline with proper intensity variation and conciseness. Next, for the textured map, we follow the painting process of artists to adjust the tone of input images to match the luminance histogram and pencil textures of real drawings. Eventually, we evaluate PencilArt via a series of comparisons to previous work, showing that our results better capture the main features of real chromatic pencil drawings and have an improved visual appearance.
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    Personalized Hand Modeling from Multiple Postures with Multi-View Color Images
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Wang, Yangang; Rao, Ruting; Zou, Changqing; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
    Personalized hand models can be utilized to synthesize high quality hand datasets, provide more possible training data for deep learning and improve the accuracy of hand pose estimation. In recent years, parameterized hand models, e.g., MANO, are widely used for obtaining personalized hand models. However, due to the low resolution of existing parameterized hand models, it is still hard to obtain high-fidelity personalized hand models. In this paper, we propose a new method to estimate personalized hand models from multiple hand postures with multi-view color images. The personalized hand model is represented by a personalized neutral hand, and multiple hand postures. We propose a novel optimization strategy to estimate the neutral hand from multiple hand postures. To demonstrate the performance of our method, we have built a multi-view system and captured more than 35 people, and each of them has 30 hand postures.We hope the estimated hand models can boost the research of highfidelity parameterized hand modeling in the future. All the hand models are publicly available on www.yangangwang.com.