OverLayBench: A Benchmark for Layout-to-Image Generation with Dense
  Overlaps
Authors: name: Bingnan Li, name: Chen-Yu Wang, name: Haiyang Xu, name: Xiang Zhang, name: Ethan Armand, name: Divyansh Srivastava, name: Xiaojun Shan, name: Zeyuan Chen, name: Jianwen Xie, name: Zhuowen Tu

Despite steady progress in layout-to-image generation, current methods still
struggle with layouts containing significant overlap between bounding boxes. We
identify two primary challenges: (1) large overlapping regions and (2)
overlapping instances with minimal semantic distinction. Through both
qualitative examples and quantitative analysis, we demonstrate how these
factors degrade generation quality. To systematically assess this issue, we
introduce OverLayScore, a novel metric that quantifies the complexity of
overlapping bounding boxes. Our analysis reveals that existing benchmarks are
biased toward simpler cases with low OverLayScore values, limiting their
effectiveness in evaluating model performance under more challenging
conditions. To bridge this gap, we present OverLayBench, a new benchmark
featuring high-quality annotations and a balanced distribution across different
levels of OverLayScore. As an initial step toward improving performance on
complex overlaps, we also propose CreatiLayout-AM, a model fine-tuned on a
curated amodal mask dataset. Together, our contributions lay the groundwork for
more robust layout-to-image generation under realistic and challenging
scenarios. Project link: https://mlpc-ucsd.github.io/OverLayBench.