385 citations · 398 across the 3 of their papers we have counts for
5 papers
Sketch Your Own GAN
Sheng-Yu Wang, David Bau, Jun-Yan Zhu
Can a user create a deep generative model by sketching a single example? Traditionally, creating a GAN model has required the collection of a large-scale dataset of exemplars and s…
Editing Conditional Radiance Fields
Steven Liu, Xiuming Zhang, Zhoutong Zhang +3
A neural radiance field (NeRF) is a scene model supporting high-quality view synthesis, optimized per scene. In this paper, we explore enabling user editing of a category-level NeR…
Ensembling with Deep Generative Views
Lucy Chai, Jun-Yan Zhu, Eli Shechtman +2
Recent generative models can synthesize "views" of artificial images that mimic real-world variations, such as changes in color or pose, simply by learning from unlabeled image col…
Anycost GANs for Interactive Image Synthesis and Editing
Ji Lin, Richard Zhang, Frieder Ganz +2
Generative adversarial networks (GANs) have enabled photorealistic image synthesis and editing. However, due to the high computational cost of large-scale generators (e.g., StyleGA…
Understanding the Role of Individual Units in a Deep Neural Network
David Bau, Jun-Yan Zhu, Hendrik Strobelt +3
Deep neural networks excel at finding hierarchical representations that solve complex tasks over large data sets. How can we humans understand these learned representations? In thi…