68 citations · 87 across the 9 of their papers we have counts for
10 papers · 1 filter
Flux Already Knows -- Activating Subject-Driven Image Generation without Training
Hao Kang, Stathi Fotiadis, Liming Jiang +5
We propose a simple yet effective zero-shot framework for subject-driven image generation using a vanilla Flux model. By framing the task as grid-based image completion and simply…
JoJoGAN: One Shot Face Stylization
Min Jin Chong, David Forsyth
A style mapper applies some fixed style to its input images (so, for example, taking faces to cartoons). This paper describes a simple procedure -- JoJoGAN -- to learn a style mapp…
StyleGAN of All Trades: Image Manipulation with Only Pretrained StyleGAN
Min Jin Chong, Hsin-Ying Lee, David Forsyth
Recently, StyleGAN has enabled various image manipulation and editing tasks thanks to the high-quality generation and the disentangled latent space. However, additional architectur…
Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval
Min Jin Chong, Wen-Sheng Chu, Abhishek Kumar +1
We present Retrieve in Style (RIS), an unsupervised framework for facial feature transfer and retrieval on real images. Recent work shows capabilities of transferring local facial…
Toward Accurate and Realistic Outfits Visualization with Attention to Details
Kedan Li, Min jin Chong, Jeffrey Zhang +1
Virtual try-on methods aim to generate images of fashion models wearing arbitrary combinations of garments. This is a challenging task because the generated image must appear reali…
GANs N' Roses: Stable, Controllable, Diverse Image to Image Translation (works for videos too!)
Min Jin Chong, David Forsyth
We show how to learn a map that takes a content code, derived from a face image, and a randomly chosen style code to an anime image. We derive an adversarial loss from our simple a…
Toward Accurate and Realistic Virtual Try-on Through Shape Matching and Multiple Warps
Kedan Li, Min Jin Chong, Jingen Liu +1
A virtual try-on method takes a product image and an image of a model and produces an image of the model wearing the product. Most methods essentially compute warps from the produc…
Effectively Unbiased FID and Inception Score and where to find them
Min Jin Chong, David Forsyth
This paper shows that two commonly used evaluation metrics for generative models, the Fréchet Inception Distance (FID) and the Inception Score (IS), are biased -- the expected valu…
Unrestricted Adversarial Examples via Semantic Manipulation
Anand Bhattad, Min Jin Chong, Kaizhao Liang +2
Machine learning models, especially deep neural networks (DNNs), have been shown to be vulnerable against adversarial examples which are carefully crafted samples with a small magn…
Learning Diverse Image Colorization
Aditya Deshpande, Jiajun Lu, Mao-Chuang Yeh +2
Colorization is an ambiguous problem, with multiple viable colorizations for a single grey-level image. However, previous methods only produce the single most probable colorization…