Browsing by Author "Wang, Lei"

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    Generative Adversarial Image Super-Resolution Through Deep Dense Skip Connections
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Zhu, Xiaobin; Li, Zhuangzi; Zhang, Xiaoyu; Li, Haisheng; Xue, Ziyu; Wang, Lei; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
    Recently, image super-resolution works based on Convolutional Neural Networks (CNNs) and Generative Adversarial Nets (GANs) have shown promising performance. However, these methods tend to generate blurry and over-smoothed super-resolved (SR) images, due to the incomplete loss function and powerless architectures of networks. In this paper, a novel generative adversarial image super-resolution through deep dense skip connections (GSR-DDNet), is proposed to solve the above-mentioned problems. It aims to take advantage of GAN's ability of modeling data distributions, so that GSR-DDNet can select informative feature representation and model the mapping across the low-quality and high-quality images in an adversarial way. The pipeline of the proposed method consists of three main components: 1) The generator of a novel dense skip connection network with the deep structure for learning robust mapping function is proposed to generate SR images from low-resolution images; 2) The feature extraction network based on VGG-19 is adopted to capture high frequency feature maps for content loss; and 3) The discriminator with Wasserstein distance is adopted to identify the overall style of SR and ground-truth images. Experiments conducted on four publicly available datasets demonstrate the superiority against the state-of-the-art methods.
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    A Robust Multi-View System for High-Fidelity Human Body Shape Reconstruction
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Zhang, Qitong; Wang, Lei; Ge, Linlin; Luo, Shan; Zhu, Taihao; Jiang, Feng; Ding, Jimmy; Feng, Jieqing; Digne, Julie and Crane, Keenan
    This paper proposes a passive multi-view system for human body shape reconstruction, namely RHF-Human, to overcome several challenges including accurate calibration and stereo matching in self-occluded and low-texture skin regions. The reconstruction process includes four steps: capture, multi-view camera calibration, dense reconstruction, and meshing. The capture system, which consists of 90 digital single-lens reflex cameras, is single-shot to avoid nonrigid deformation of the human body. Two technical contributions are made: (1) a two-step robust multi-view calibration approach that improves calibration accuracy and saves calibration time for each new human body acquired and (2) an accurate PatchMatch multi-view stereo method for dense reconstruction to perform correct matching in self-occluded and low-texture skin regions and to reduce the noise caused by body hair. Experiments on models of various genders, poses, and skin with different amounts of body hair show the robustness of the proposed system. A high-fidelity human body shape dataset with 227 models is constructed, and the average accuracy is within 1.5 mm. The system provides a new scheme for the accurate reconstruction of nonrigid human models based on passive vision and has good potential in fashion design and health care.