• Papers

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• [1] Kun Li, Jingyu Yang, Leijie Liu, Ronan Boulic, Yukun Lai, Yebin Liu, Yubin Li, and Eray Molla, “SPA: Sparse Photorealistic Animation Using a Single RGB-D Camera”, IEEE Transactions on Circuits and System for Video Technology (Special Issue on Augmented Video), vol 27, no. 4, pp. 771-783, 2017.
• [2] Kun Li, Yanming Zhu, Jianmin Jiang and Jingyu Yang, “Video Super-resolution Using an Adaptived Superpixel-guided Auto-Regeressive Model”, Pattern Recognition, vol. 51, no. 3, pp. 59-71, 2016.
• • [3] Kun Li, Jingyu Yang and Jianmin Jiang, “Nonrigid structure from motion via sparse representation”, IEEE Trans. Cybernetics, vol. 45, no. 8, pp. 1401-1413, 2015.
  • [4] Jingyu Yang, Ke Li, Kun Li, Yukun Lai, “Sparse Non-rigid Registration of 3D Shapes”, Computer Graphics Forum, vol. 34, no. 5, pp. 89-99, 2015.
  • [5] Jingyu Yang, Ziqiao Gan, Kun Li, Chunping Hou, “Graph-based Segmentation for RGB-D Data Using 3-D Geometry Enhanced Superpixels”, IEEE Trans. Cybernetics, vol. 45, no. 5, pp. 913-926, 2015.
  • [6] Xinchen Ye, Jingyu Yang, Xin Sun, and Kun Li. Foreground-Background Separation From Video Clips via Motion-Assisted Matrix Restoration. IEEE Trans. Circuits and Systems for Video Technology, vol. 25, no. 11, pp. 1721-1734, 2015.
  • [7] Jingyu Yang, Xinchen Ye, Kun Li, Chunping Hou, Yao Wang, “Color-guided Depth Recovery from RGB-D Data Using an Adaptive Auto-regressive Model”, IEEE Trans. Image Processing, vol. 23, no. 8, pp. 3443-3458, 2014.
  • [8] Yanming Zhu, Kun Li, and Jianmin Jiang, “Video Super-Resolution Based on Automatic Key-Frame Selection and Feature-Guided Variational Optical Flow”, Signal Processing: Image Communication, 29(8), 875-886, 2014.
  • [9] Kun Li, Qionghai Dai, Wenli Xu, et al., “Temporal-dense dynamic 3D reconstruction with low frame rate cameras”, IEEE Journal of Selected Topics in Signal Processing, vol. 6, no. 5, pp. 447-459, 2012.
  • [10] Kun Li, Qionghai Dai, Wenli Xu, Jingyu Yang and Jianmin Jiang, “Three-Dimensional motion estimation via matrix completion”, IEEE Trans. Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 42, no. 2, pp. 539-551, 2012.
  • [11] Kun Li, Qionghai Dai and Wenli Xu, “Markless shape and motion capture from video sequences”, IEEE Trans. Circuits and System for Video Technology, vol. 21, no. 3, pp. 320-334, 2011.
  • [12] Kun Li, Qionghai Dai and Wenli Xu, “Collaborative color calibration for multi-camera systems”, Signal Processing: Image Communication, vol. 26, no. 1, pp. 48-60, 2011.

• Patents

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• [1] 基于小波变换的单相机视频三维重建方法
• [2] 时空联合多视角视频插值及三维建模方法
• • [3] 基于特征导向变分光流的视频超分辨率方法
  • [4] 采用环形低帧摄像机阵列对高速运动物体建模的方法
  • [5] 一种环形摄像机阵列校准系统及其方法
  • [6] 一种对多台摄像机进行全局颜色校准的方法
  • [7] 三维模型的捕捉及重建方法和系统
  • [8] 基于自适应的超像素导向自回归模型的视频超分辨率方法
  • [9] 基于运动信息和矩阵填充的视频背景恢复方法
  • [10] 基于飞行时间TOF相机的深度计算成像方法
  • [11] 基于散斑结构光深度相机的多视点计算成像方法
  • [12] 采用自回归模型对深度图进行超分辨率重建的方法
  • [13] 基于感兴趣深度的立体图像压缩方法
  • [14] 基于稀疏表示理论的超分辨率图像获取方法
  • [15] 基于稀疏表示的非刚性表面对齐方法
  • [16] 基于加权双稀疏约束的非刚性表面配准方法
  • [17] 可变形物体的全局非刚性配准与重建
  • [18] 基于图论的低秩矩阵恢复三维骨架方法
  • [19] 基于体感相机Kinect v2.0的真实感动画生成方法
  • [20] 基于Kinect深度相机的人体分割方法
  • [21] 一种雾霾环境下的深度计算方法
  • [22] 基于图像的雾霾PM2.5值估计方法
  • [23] 基于L1范数约束的RGB-D图像本征分解方法
  • [24] 基于低秩矩阵分析的三维骨架修复方法
  • [25] 基于低秩矩阵重建和稀疏表示的行列缺失图像填充方法

• Honors & Awards

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• [1] Excellent Young Scientists Fund of National Natural Science Foundation of China
• [2] 天津市杰出青年
• • [3] 天津大学科技领军创新人才“卓荦计划”（首届，全校10人）
  • [4] World's FIRST 10K Best Paper Award – Platinum Award IEEE ICME 2017
  • [5] CCF Senior Member
  • [6] Peiyang Scholar Program of Tianjin University