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Found 9 papers, 6 papers with code
Facial Reenactment Through a Personalized Generator
In this paper, we propose a novel method for facial reenactment using a personalized generator.
Domain Expansion of Image Generators
First, we note the generator contains a meaningful, pretrained latent space.
MyStyle: A Personalized Generative Prior
Given a small reference set of portrait images of a person (~100), we tune the weights of a pretrained StyleGAN face generator to form a local, low-dimensional, personalized manifold in the latent space.
State-of-the-Art in the Architecture, Methods and Applications of StyleGAN
Of these, StyleGAN offers a fascinating case study, owing to its remarkable visual quality and an ability to support a large array of downstream tasks.
StyleAlign: Analysis and Applications of Aligned StyleGAN Models
Several works already utilize some basic properties of aligned StyleGAN models to perform image-to-image translation.
LARGE: Latent-Based Regression through GAN Semantics
For modern generative frameworks, this semantic encoding manifests as smooth, linear directions which affect image attributes in a disentangled manner.
Designing an Encoder for StyleGAN Image Manipulation
We then suggest two principles for designing encoders in a manner that allows one to control the proximity of the inversions to regions that StyleGAN was originally trained on.
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
We present a generic image-to-image translation framework, pixel2style2pixel (pSp).
Face Identity Disentanglement via Latent Space Mapping
Learning disentangled representations of data is a fundamental problem in artificial intelligence.
