Rethinking The Training And Evaluation of Rich-Context Layout-to-Image Generation
<|reference_start|>Rethinking The Training And Evaluation of Rich-Context Layout-to-Image Generation: 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.<|reference_end|>
@article{cheng2024rethinking,
    title={Rethinking The Training And Evaluation of Rich-Context Layout-to-Image
  Generation},
    author={Jiaxin Cheng, Zixu Zhao, Tong He, Tianjun Xiao, Yicong Zhou, Zheng
  Zhang},
    journal={arXiv preprint arXiv:2409.04847},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.04847},
    primaryClass={cs.CV}
}