385 citations · 832 across the 26 of their papers we have counts for
9 papers · 1 filter
Dense Text-to-Image Generation with Attention Modulation
Yunji Kim, Jiyoung Lee, Jin-Hwa Kim +2
Existing text-to-image diffusion models struggle to synthesize realistic images given dense captions, where each text prompt provides a detailed description for a specific image re…
Evaluating Data Attribution for Text-to-Image Models
Sheng-Yu Wang, Alexei A. Efros, Jun-Yan Zhu +1
While large text-to-image models are able to synthesize "novel" images, these images are necessarily a reflection of the training data. The problem of data attribution in such mode…
Controllable Visual-Tactile Synthesis
Ruihan Gao, Wenzhen Yuan, Jun-Yan Zhu
Deep generative models have various content creation applications such as graphic design, e-commerce, and virtual Try-on. However, current works mainly focus on synthesizing realis…
Expressive Text-to-Image Generation with Rich Text
Songwei Ge, Taesung Park, Jun-Yan Zhu +1
Plain text has become a prevalent interface for text-to-image synthesis. However, its limited customization options hinder users from accurately describing desired outputs. For exa…
Ablating Concepts in Text-to-Image Diffusion Models
Nupur Kumari, Bingliang Zhang, Sheng-Yu Wang +3
Large-scale text-to-image diffusion models can generate high-fidelity images with powerful compositional ability. However, these models are typically trained on an enormous amount…
Scaling up GANs for Text-to-Image Synthesis
Minguk Kang, Jun-Yan Zhu, Richard Zhang +4
The recent success of text-to-image synthesis has taken the world by storm and captured the general public's imagination. From a technical standpoint, it also marked a drastic chan…
Zero-shot Image-to-Image Translation
Gaurav Parmar, Krishna Kumar Singh, Richard Zhang +3
Large-scale text-to-image generative models have shown their remarkable ability to synthesize diverse and high-quality images. However, it is still challenging to directly apply th…
3D-aware Blending with Generative NeRFs
Hyunsu Kim, Gayoung Lee, Yunjey Choi +2
Image blending aims to combine multiple images seamlessly. It remains challenging for existing 2D-based methods, especially when input images are misaligned due to differences in 3…
Domain Expansion of Image Generators
Yotam Nitzan, Michaël Gharbi, Richard Zhang +4
Can one inject new concepts into an already trained generative model, while respecting its existing structure and knowledge? We propose a new task - domain expansion - to address t…