18 citations · 24 across the 10 of their papers we have counts for
17 papers · 1 filter
Collaborative Learning for Hand and Object Reconstruction with Attention-guided Graph Convolution
Tze Ho Elden Tse, Kwang In Kim, Ales Leonardis +1
Estimating the pose and shape of hands and objects under interaction finds numerous applications including augmented and virtual reality. Existing approaches for hand and object re…
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…
GaussiGAN: Controllable Image Synthesis with 3D Gaussians from Unposed Silhouettes
Youssef A. Mejjati, Isa Milefchik, Aaron Gokaslan +3
We present an algorithm that learns a coarse 3D representation of objects from unposed multi-view 2D mask supervision, then uses it to generate detailed mask and image texture. In…
Look here! A parametric learning based approach to redirect visual attention
Youssef Alami Mejjati, Celso F. Gomez, Kwang In Kim +2
Across photography, marketing, and website design, being able to direct the viewer's attention is a powerful tool. Motivated by professional workflows, we introduce an automatic me…
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,…
Generating Object Stamps
Youssef Alami Mejjati, Zejiang Shen, Michael Snower +4
We present an algorithm to generate diverse foreground objects and composite them into background images using a GAN architecture. Given an object class, a user-provided bounding b…