55 citations · 68 across the 8 of their papers we have counts for
8 papers
For A More Comprehensive Evaluation of 6DoF Object Pose Tracking
Yang Li, Fan Zhong, Xin Wang +4
Previous evaluations on 6DoF object pose tracking have presented obvious limitations along with the development of this area. In particular, the evaluation protocols are not unifie…
Guided Linear Upsampling
Shuangbing Song, Fan Zhong, Tianju Wang +2
Guided upsampling is an effective approach for accelerating high-resolution image processing. In this paper, we propose a simple yet effective guided upsampling method. Each pixel…
Large-displacement 3D Object Tracking with Hybrid Non-local Optimization
Xuhui Tian, Xinran Lin, Fan Zhong +1
Optimization-based 3D object tracking is known to be precise and fast, but sensitive to large inter-frame displacements. In this paper we propose a fast and effective non-local 3D…
BCOT: A Markerless High-Precision 3D Object Tracking Benchmark
Jiachen Li, Bin Wang, Shiqiang Zhu +6
Template-based 3D object tracking still lacks a high-precision benchmark of real scenes due to the difficulty of annotating the accurate 3D poses of real moving video objects witho…
GazeCorrection:Self-Guided Eye Manipulation in the wild using Self-Supervised Generative Adversarial Networks
Jichao Zhang, Meng Sun, Jingjing Chen +4
Gaze correction aims to redirect the person's gaze into the camera by manipulating the eye region, and it can be considered as a specific image resynthesis problem. Gaze correction…
Deeply Supervised Depth Map Super-Resolution as Novel View Synthesis
Xibin Song, Yuchao Dai, Xueying Qin
Deep convolutional neural network (DCNN) has been successfully applied to depth map super-resolution and outperforms existing methods by a wide margin. However, there still exist t…
Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation
Jichao Zhang, Yezhi Shu, Songhua Xu +4
Recent Image-to-Image Translation algorithms have achieved significant progress in neural style transfer and image attribute manipulation tasks. However, existing approaches requir…
Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural Network
Xibin Song, Yuchao Dai, Xueying Qin
Depth image super-resolution is an extremely challenging task due to the information loss in sub-sampling. Deep convolutional neural network have been widely applied to color image…