Browsing by Author "Han, Xiaoguang"

Now showing items 1-5 of 5

    • 3D Keypoint Estimation Using Implicit Representation Learning 

      Zhu, Xiangyu; Du, Dong; Huang, Haibin; Ma, Chongyang; Han, Xiaoguang (The Eurographics Association and John Wiley & Sons Ltd., 2023)
      In this paper, we tackle the challenging problem of 3D keypoint estimation of general objects using a novel implicit representation. Previous works have demonstrated promising results for keypoint prediction through direct ...

    • DiffusionPointLabel: Annotated Point Cloud Generation with Diffusion Model 

      Li, Tingting; Fu, Yunfei; Han, Xiaoguang; Liang, Hui; Zhang, Jian Jun; Chang, Jian (The Eurographics Association and John Wiley & Sons Ltd., 2022)
      Point cloud generation aims to synthesize point clouds that do not exist in supervised dataset. Generating a point cloud with certain semantic labels remains an under-explored problem. This paper proposes a formulation ...

    • GA-Sketching: Shape Modeling from Multi-View Sketching with Geometry-Aligned Deep Implicit Functions 

      Zhou, Jie; Luo, Zhongjin; Yu, Qian; Han, Xiaoguang; Fu, Hongbo (The Eurographics Association and John Wiley & Sons Ltd., 2023)
      Sketch-based shape modeling aims to bridge the gap between 2D drawing and 3D modeling by providing an intuitive and accessible approach to create 3D shapes from 2D sketches. However, existing methods still suffer from ...

    • Learning Part Generation and Assembly for Sketching Man‐Made Objects 

      Du, Dong; Zhu, Heming; Nie, Yinyu; Han, Xiaoguang; Cui, Shuguang; Yu, Yizhou; Liu, Ligang (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021)
      Modeling 3D objects on existing software usually requires a heavy amount of interactions, especially for users who lack basic knowledge of 3D geometry. Sketch‐based modeling is a solution to ease the modelling procedure ...

    • Two-phase Hair Image Synthesis by Self-Enhancing Generative Model 

      Qiu, Haonan; Wang, Chuan; Zhu, Hang; zhu, xiangyu; Gu, Jinjin; Han, Xiaoguang (The Eurographics Association and John Wiley & Sons Ltd., 2019)
      Generating plausible hair image given limited guidance, such as sparse sketches or low-resolution image, has been made possible with the rise of Generative Adversarial Networks (GANs). Traditional image-to-image translation ...