Abstract
<abstract><p>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.</p></abstract>
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
