Integrating View Conditions for Image Synthesis
Jinbin Bai, Zhen Dong, Aosong Feng, Xiao Zhang, Tian Ye, Kaicheng Zhou
Abstract
In the field of image processing, applying intricate semantic modifications within existing images remains an enduring challenge. This paper introduces a pioneering framework that integrates viewpoint information to enhance the control of image editing tasks, especially for interior design scenes. By surveying existing object editing methodologies, we distill three essential criteria --- consistency, controllability, and harmony --- that should be met for an image editing method. In contrast to previous approaches, our framework takes the lead in satisfying all three requirements for addressing the challenge of image synthesis. Through comprehensive experiments, encompassing both quantitative assessments and qualitative comparisons with contemporary state-of-the-art methods, we present compelling evidence of our framework's superior performance across multiple dimensions. This work establishes a promising avenue for advancing image synthesis techniques and empowering precise object modifications while preserving the visual coherence of the entire composition.
BibTeX
@inproceedings{ijcai2024p840,
title = {Integrating View Conditions for Image Synthesis},
author = {Bai, Jinbin and Dong, Zhen and Feng, Aosong and Zhang, Xiao and Ye, Tian and Zhou, Kaicheng},
booktitle = {Proceedings of the Thirty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-24}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Kate Larson},
pages = {7591--7599},
year = {2024},
month = {8},
note = {AI, Arts & Creativity},
doi = {10.24963/ijcai.2024/840},
url = {https://doi.org/10.24963/ijcai.2024/840},
}