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Found 8 papers, 2 papers with code
For A More Comprehensive Evaluation of 6DoF Object Pose Tracking
The limitations of previous scoring methods and error metrics are analyzed, based on which we introduce our improved evaluation methods.
Guided Linear Upsampling
We demonstrate the advantages of our method for both interactive image editing and real-time high-resolution video processing.
Large-displacement 3D Object Tracking with Hybrid Non-local Optimization
Optimization-based 3D object tracking is known to be precise and fast, but sensitive to large inter-frame displacements.
BCOT: A Markerless High-Precision 3D Object Tracking Benchmark
Our new benchmark dataset contains 20 textureless objects, 22 scenes, 404 video sequences and 126K images captured in real scenes.
GazeCorrection:Self-Guided Eye Manipulation in the wild using Self-Supervised Generative Adversarial Networks
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.
Deeply Supervised Depth Map Super-Resolution as Novel View Synthesis
However, there still exist two major issues with these DCNN based depth map super-resolution methods that hinder the performance: i) The low-resolution depth maps either need to be up-sampled before feeding into the network or substantial deconvolution has to be used; and ii) The supervision (high-resolution depth maps) is only applied at the end of the network, thus it is difficult to handle large up-sampling factors, such as $\times 8, \times 16$.
Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation
To overcome such a key limitation, we propose Sparsely Grouped Generative Adversarial Networks (SG-GAN) as a novel approach that can translate images on sparsely grouped datasets where only a few samples for training are labelled.
Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural Network
In this paper, we bridge up the gap and extend the success of deep convolutional neural network to depth super-resolution.
