activity
20162021
most citedFew-shot Video-to-Video Synthesis

8 citations · 25 across the 4 of their papers we have counts for

collaborators

13 papers

cs.CV2021

View Generalization for Single Image Textured 3D Models

Anand Bhattad, Aysegul Dundar, Guilin Liu +2

Humans can easily infer the underlying 3D geometry and texture of an object only from a single 2D image. Current computer vision methods can do this, too, but suffer from view gene…

cs.CV20215 cited

DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision

Shiyi Lan, Zhiding Yu, Christopher Choy +5

We introduce DiscoBox, a novel framework that jointly learns instance segmentation and semantic correspondence using bounding box supervision. Specifically, we propose a self-ensem…

cs.CV20206 cited

Transposer: Universal Texture Synthesis Using Feature Maps as Transposed Convolution Filter

Guilin Liu, Rohan Taori, Ting-Chun Wang +6

Conventional CNNs for texture synthesis consist of a sequence of (de)-convolution and up/down-sampling layers, where each layer operates locally and lacks the ability to capture th…

cs.CV2020

Panoptic-based Image Synthesis

Aysegul Dundar, Karan Sapra, Guilin Liu +2

Conditional image synthesis for generating photorealistic images serves various applications for content editing to content generation. Previous conditional image synthesis algorit…

cs.CV20198 cited

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…

cs.CV2019

Unsupervised Video Interpolation Using Cycle Consistency

Fitsum A. Reda, Deqing Sun, Aysegul Dundar +6

Learning to synthesize high frame rate videos via interpolation requires large quantities of high frame rate training videos, which, however, are scarce, especially at high resolut…