10 citations · 16 across the 6 of their papers we have counts for
6 papers
The GAN is dead; long live the GAN! A Modern GAN Baseline
Yiwen Huang, Aaron Gokaslan, Volodymyr Kuleshov +1
There is a widely-spread claim that GANs are difficult to train, and GAN architectures in the literature are littered with empirical tricks. We provide evidence against this claim…
TöRF: Time-of-Flight Radiance Fields for Dynamic Scene View Synthesis
Benjamin Attal, Eliot Laidlaw, Aaron Gokaslan +4
Neural networks can represent and accurately reconstruct radiance fields for static 3D scenes (e.g., NeRF). Several works extend these to dynamic scenes captured with monocular vid…
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…
MatryODShka: Real-time 6DoF Video View Synthesis using Multi-Sphere Images
Benjamin Attal, Selena Ling, Aaron Gokaslan +2
We introduce a method to convert stereo 360° (omnidirectional stereo) imagery into a layered, multi-sphere image representation for six degree-of-freedom (6DoF) rendering. Stereo 3…
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…
Improving Shape Deformation in Unsupervised Image-to-Image Translation
Aaron Gokaslan, Vivek Ramanujan, Daniel Ritchie +2
Unsupervised image-to-image translation techniques are able to map local texture between two domains, but they are typically unsuccessful when the domains require larger shape chan…