2 citations · 4 across the 2 of their papers we have counts for
6 papers · 1 filter
DynaDog+T: A Parametric Animal Model for Synthetic Canine Image Generation
Jake Deane, Sinead Kearney, Kwang In Kim +1
Synthetic data is becoming increasingly common for training computer vision models for a variety of tasks. Notably, such data has been applied in tasks related to humans such as 3D…
RGBD-Dog: Predicting Canine Pose from RGBD Sensors
Sinead Kearney, Wenbin Li, Martin Parsons +2
The automatic extraction of animal \reb{3D} pose from images without markers is of interest in a range of scientific fields. Most work to date predicts animal pose from RGB images,…
Unsupervised Attention-guided Image to Image Translation
Youssef A. Mejjati, Christian Richardt, James Tompkin +2
Current unsupervised image-to-image translation techniques struggle to focus their attention on individual objects without altering the background or the way multiple objects inter…
Video Interpolation using Optical Flow and Laplacian Smoothness
Wenbin Li, Darren Cosker
Non-rigid video interpolation is a common computer vision task. In this paper we present an optical flow approach which adopts a Laplacian Cotangent Mesh constraint to enhance the…
Blur Robust Optical Flow using Motion Channel
Wenbin Li, Yang Chen, JeeHang Lee +2
It is hard to estimate optical flow given a realworld video sequence with camera shake and other motion blur. In this paper, we first investigate the blur parameterization for vide…
Drift Robust Non-rigid Optical Flow Enhancement for Long Sequences
Wenbin Li, Darren Cosker, Matthew Brown
It is hard to densely track a nonrigid object in long term, which is a fundamental research issue in the computer vision community. This task often relies on estimating pairwise co…