@inproceedings{
cheng2024rethinking,
title={Rethinking The Training And Evaluation of Rich-Context Layout-to-Image Generation},
author={Jiaxin Cheng and Zixu Zhao and Tong He and Tianjun Xiao and Yicong Zhou and Zheng Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=83e3DPVrFC}
}
Recent advancements in generative models have significantly enhanced their capacity for image generation, enabling a wide range of applications such as image editing, completion and video editing. A specialized area within generative modeling is layout-to-image (L2I) generation, where predefined layouts of objects guide the generative process. In this study, we introduce a novel regional cross-attention module tailored to enrich layout-to-image generation. This module notably improves the representation of layout regions, particularly in scenarios where existing methods struggle with highly complex and detailed textual descriptions. Moreover, while current open-vocabulary L2I methods are trained in an open-set setting, their evaluations often occur in closed-set environments. To bridge this gap, we propose two metrics to assess L2I performance in open-vocabulary scenarios. Additionally, we conduct a comprehensive user study to validate the consistency of these metrics with human preferences.
Rethinking The Training And Evaluation of Rich-Context Layout-to-Image Generation
Jiaxin Cheng Zixu Zhao Tong He Tianjun Xiao Yicong Zhou Zheng Zhang