activity
20192023
most citedImage Augmentations for GAN Training

116 citations · 174 across the 7 of their papers we have counts for

collaborators

9 papers

cs.CV20231 cited

Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency

Tianhong Li, Sangnie Bhardwaj, Yonglong Tian +6

Current vision-language generative models rely on expansive corpora of paired image-text data to attain optimal performance and generalization capabilities. However, automatically…

cs.CV20231 cited

StoryBench: A Multifaceted Benchmark for Continuous Story Visualization

Emanuele Bugliarello, Hernan Moraldo, Ruben Villegas +7

Generating video stories from text prompts is a complex task. In addition to having high visual quality, videos need to realistically adhere to a sequence of text prompts whilst be…

cs.CV20231 cited

Learning Disentangled Prompts for Compositional Image Synthesis

Kihyuk Sohn, Albert Shaw, Yuan Hao +5

We study domain-adaptive image synthesis, the problem of teaching pretrained image generative models a new style or concept from as few as one image to synthesize novel images, to…

cs.CV20235 cited

Identity Encoder for Personalized Diffusion

Yu-Chuan Su, Kelvin C. K. Chan, Yandong Li +5

Many applications can benefit from personalized image generation models, including image enhancement, video conferences, just to name a few. Existing works achieved personalization…

cs.CV202321 cited

Taming Encoder for Zero Fine-tuning Image Customization with Text-to-Image Diffusion Models

Xuhui Jia, Yang Zhao, Kelvin C. K. Chan +6

This paper proposes a method for generating images of customized objects specified by users. The method is based on a general framework that bypasses the lengthy optimization requi…

cs.CV20231 cited

VQ3D: Learning a 3D-Aware Generative Model on ImageNet

Kyle Sargent, Jing Yu Koh, Han Zhang +5

Recent work has shown the possibility of training generative models of 3D content from 2D image collections on small datasets corresponding to a single object class, such as human…