Learning to Generate Multiple Objects from Dense and Occluded Layouts

• URL: http://arxiv.org/abs/2607.03488v1
• Date: Fri, 03 Jul 2026 16:56:01 GMT
• Title: Learning to Generate Multiple Objects from Dense and Occluded Layouts
• Abstract summary: In dense scenes, overlapping instances collapse into indistinguishable structures despite appearing visually plausible.<n>We address this through layout-aware attention biases that softly bias token interactions toward region-consistent grouping.<n>Our approach substantially improves count accuracy and prevents instance merging while preserving image quality.
• Score: 8.60490943852385
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Text-to-image diffusion models fail to generate correct object counts in dense scenes, where overlapping instances collapse into indistinguishable structures despite appearing visually plausible. We identify this as instance ownership collapse: tokens from overlapping objects interact freely through attention, while heavily occluded instances receive weak supervision due to their small visible areas. We address this through layout-aware attention biases that softly bias token interactions toward region-consistent grouping and suppress cross-instance leakage, paired with an amodal-balanced loss that amplifies gradients for occluded objects based on their occlusion level. To enable systematic evaluation, we introduce OverlapDepth-45K, a benchmark of densely overlapping scenes with amodal supervision. Our approach substantially improves count accuracy and prevents instance merging while preserving image quality. Project page: https://bachngoh.github.io/AIBL

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