1 code implementation • CVPR 2020 • Zhouxia Wang, Jiawei Zhang, Mude Lin, Jiong Wang, Ping Luo, Jimmy Ren
Automatically selecting exposure bracketing (images exposed differently) is important to obtain a high dynamic range image by using multi-exposure fusion.
1 code implementation • 11 Apr 2019 • Luyang Wang, Yan Chen, Zhenhua Guo, Keyuan Qian, Mude Lin, Hongsheng Li, Jimmy S. Ren
We observe that recent innovation in this area mainly focuses on new techniques that explicitly address the generalization issue when using this dataset, because this database is constructed in a highly controlled environment with limited human subjects and background variations.
Ranked #68 on 3D Human Pose Estimation on Human3.6M
1 code implementation • CVPR 2018 • Yue Luo, Jimmy Ren, Mude Lin, Jiahao Pang, Wenxiu Sun, Hongsheng Li, Liang Lin
The resulting model outperforms all the previous monocular depth estimation methods as well as the stereo block matching method in the challenging KITTI dataset by only using a small number of real training data.
Ranked #43 on Monocular Depth Estimation on KITTI Eigen split
no code implementations • CVPR 2017 • Mude Lin, Liang Lin, Xiaodan Liang, Keze Wang, Hui Cheng
3D human articulated pose recovery from monocular image sequences is very challenging due to the diverse appearances, viewpoints, occlusions, and also the human 3D pose is inherently ambiguous from the monocular imagery.
Ranked #22 on 3D Human Pose Estimation on HumanEva-I
no code implementations • 13 Feb 2016 • Shuye Zhang, Mude Lin, Tianshui Chen, Lianwen Jin, Liang Lin
Maximally stable extremal regions (MSER), which is a popular method to generate character proposals/candidates, has shown superior performance in scene text detection.