30 citations · 57 across the 4 of their papers we have counts for
10 papers
GANcraft: Unsupervised 3D Neural Rendering of Minecraft Worlds
Zekun Hao, Arun Mallya, Serge Belongie +1
We present GANcraft, an unsupervised neural rendering framework for generating photorealistic images of large 3D block worlds such as those created in Minecraft. Our method takes a…
See through Gradients: Image Batch Recovery via GradInversion
Hongxu Yin, Arun Mallya, Arash Vahdat +3
Training deep neural networks requires gradient estimation from data batches to update parameters. Gradients per parameter are averaged over a set of data and this has been presume…
One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing
Ting-Chun Wang, Arun Mallya, Ming-Yu Liu
We propose a neural talking-head video synthesis model and demonstrate its application to video conferencing. Our model learns to synthesize a talking-head video using a source ima…
Generative Adversarial Networks for Image and Video Synthesis: Algorithms and Applications
Ming-Yu Liu, Xun Huang, Jiahui Yu +2
The generative adversarial network (GAN) framework has emerged as a powerful tool for various image and video synthesis tasks, allowing the synthesis of visual content in an uncond…
World-Consistent Video-to-Video Synthesis
Arun Mallya, Ting-Chun Wang, Karan Sapra +1
Video-to-video synthesis (vid2vid) aims for converting high-level semantic inputs to photorealistic videos. While existing vid2vid methods can achieve short-term temporal consisten…
Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion
Hongxu Yin, Pavlo Molchanov, Zhizhong Li +5
We introduce DeepInversion, a new method for synthesizing images from the image distribution used to train a deep neural network. We 'invert' a trained network (teacher) to synthes…