226 citations · 457 across the 16 of their papers we have counts for
24 papers · 1 filter
AI Art Neural Constellation: Revealing the Collective and Contrastive State of AI-Generated and Human Art
Faizan Farooq Khan, Diana Kim, Divyansh Jha +5
Discovering the creative potentials of a random signal to various artistic expressions in aesthetic and conceptual richness is a ground for the recent success of generative machine…
Diffusion Guided Domain Adaptation of Image Generators
Kunpeng Song, Ligong Han, Bingchen Liu +2
Can a text-to-image diffusion model be used as a training objective for adapting a GAN generator to another domain? In this paper, we show that the classifier-free guidance can be…
Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis
Bingchen Liu, Yizhe Zhu, Kunpeng Song +1
Training Generative Adversarial Networks (GAN) on high-fidelity images usually requires large-scale GPU-clusters and a vast number of training images. In this paper, we study the f…
Self-Supervised Sketch-to-Image Synthesis
Bingchen Liu, Yizhe Zhu, Kunpeng Song +1
Imagining a colored realistic image from an arbitrarily drawn sketch is one of the human capabilities that we eager machines to mimic. Unlike previous methods that either requires…
TIME: Text and Image Mutual-Translation Adversarial Networks
Bingchen Liu, Kunpeng Song, Yizhe Zhu +2
Focusing on text-to-image (T2I) generation, we propose Text and Image Mutual-Translation Adversarial Networks (TIME), a lightweight but effective model that jointly learns a T2I ge…
Sketch-to-Art: Synthesizing Stylized Art Images From Sketches
Bingchen Liu, Kunpeng Song, Ahmed Elgammal
We propose a new approach for synthesizing fully detailed art-stylized images from sketches. Given a sketch, with no semantic tagging, and a reference image of a specific style, th…