14 citations · 32 across the 6 of their papers we have counts for
7 papers
Towards Markerless Grasp Capture
Samarth Brahmbhatt, Charles C. Kemp, James Hays
Humans excel at grasping objects and manipulating them. Capturing human grasps is important for understanding grasping behavior and reconstructing it realistically in Virtual Reali…
Kernel Mean Matching for Content Addressability of GANs
Wittawat Jitkrittum, Patsorn Sangkloy, Muhammad Waleed Gondal +3
We propose a novel procedure which adds "content-addressability" to any given unconditional implicit model e.g., a generative adversarial network (GAN). The procedure allows users…
Let's Dance: Learning From Online Dance Videos
Daniel Castro, Steven Hickson, Patsorn Sangkloy +4
In recent years, deep neural network approaches have naturally extended to the video domain, in their simplest case by aggregating per-frame classifications as a baseline for actio…
Revisiting IM2GPS in the Deep Learning Era
Nam Vo, Nathan Jacobs, James Hays
Image geolocalization, inferring the geographic location of an image, is a challenging computer vision problem with many potential applications. The recent state-of-the-art approac…
On Convergence and Stability of GANs
Naveen Kodali, Jacob Abernethy, James Hays +1
We propose studying GAN training dynamics as regret minimization, which is in contrast to the popular view that there is consistent minimization of a divergence between real and ge…
Super-resolution Using Constrained Deep Texture Synthesis
Libin Sun, James Hays
Hallucinating high frequency image details in single image super-resolution is a challenging task. Traditional super-resolution methods tend to produce oversmoothed output images d…