OverLayBench: A Benchmark for Layout-to-Image Generation with Dense
  Overlaps
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.
68d726280177a6054b013727
Bingnan Li
68d726280177a6054b013728
Chen-Yu Wang
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Haiyang Xu
68d726280177a6054b01372a
Xiang Zhang
68d726280177a6054b01372b
Ethan Armand
68d726280177a6054b01372c
Divyansh Srivastava
68d726280177a6054b01372d
Xiaojun Shan
68d726280177a6054b01372e
Zeyuan Chen
68d726280177a6054b01372f
Jianwen Xie
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Zhuowen Tu
OverLayBench: A Benchmark for Layout-to-Image Generation with Dense
  Overlaps
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.
A new benchmark and metric are introduced to evaluate layout-to-image generation models on complex overlapping bounding boxes, along with a fine-tuned model to improve performance.
layout-to-image generation bounding boxes OverLayScore OverLayBench CreatiLayout-AM amodal mask dataset