@article{Zhang2023, 
author = {Shixiong Zhang and Jiao Li and Lu Yang},
title = {Survey on low-level controllable image synthesis with deep learning},
year = {2023},
journal = {Electronic Research Archive},
volume = {31},
number = {12},
pages = {7385-7426},
keywords = {image synthesis, low-level controllable, NeRF, GAN, diffusion model},
url = {https://www.sciopen.com/article/10.3934/era.2023374},
doi = {10.3934/era.2023374},
abstract = {Deep learning, particularly generative models, has inspired controllable image synthesis methods and applications. These approaches aim to generate specific visual content using latent prompts. To explore low-level controllable image synthesis for precise rendering and editing tasks, we present a survey of recent works in this field using deep learning. We begin by discussing data sets and evaluation indicators for low-level controllable image synthesis. Then, we review the state-of-the-art research on geometrically controllable image synthesis, focusing on viewpoint/pose and structure/shape controllability. Additionally, we cover photometrically controllable image synthesis methods for 3D re-lighting studies. While our focus is on algorithms, we also provide a brief overview of related applications, products and resources for practitioners.}
}