34 citations · 146 across the 13 of their papers we have counts for
29 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…
Style Example-Guided Text Generation using Generative Adversarial Transformers
Kuo-Hao Zeng, Mohammad Shoeybi, Ming-Yu Liu
We introduce a language generative model framework for generating a styled paragraph based on a context sentence and a style reference example. The framework consists of a style en…
Learning to Generate Multiple Style Transfer Outputs for an Input Sentence
Kevin Lin, Ming-Yu Liu, Ming-Ting Sun +1
Text style transfer refers to the task of rephrasing a given text in a different style. While various methods have been proposed to advance the state of the art, they often assume…
UNAS: Differentiable Architecture Search Meets Reinforcement Learning
Arash Vahdat, Arun Mallya, Ming-Yu Liu +1
Neural architecture search (NAS) aims to discover network architectures with desired properties such as high accuracy or low latency. Recently, differentiable NAS (DNAS) has demons…
Few-shot Video-to-Video Synthesis
Ting-Chun Wang, Ming-Yu Liu, Andrew Tao +3
Video-to-video synthesis (vid2vid) aims at converting an input semantic video, such as videos of human poses or segmentation masks, to an output photorealistic video. While the sta…
Neural Turtle Graphics for Modeling City Road Layouts
Hang Chu, Daiqing Li, David Acuna +6
We propose Neural Turtle Graphics (NTG), a novel generative model for spatial graphs, and demonstrate its applications in modeling city road layouts. Specifically, we represent the…