← 研踪 PaperTrailarXiv:2604.10675 · v2
HiddenObjects: Scalable Spatial Prior Extraction for Object Placement from Diffusion Models
首次提交 2026-04-12 22:59
版本更新 2026-11-05 08:13
雷达观测 2026-11-06 11:06
摘要
We learn explicit, class-conditioned spatial priors for object placement in natural scenes by distilling placement knowledge from text-conditioned diffusion models. Prior methods rely on sparse manual annotations or on inpainting object-removal pipelines, whose artifacts promote shortcut learning. To address these limitations, we propose a scalable pipeline that evaluates dense candidate placements by inserting objects into clean backgrounds. This produces HiddenObjects, a dataset of 27M bounding-box annotations (2M positives) across 27K diverse scenes and object categories. Human evaluation prefers our image editing composites over those guided by sparse annotations in 65.6% of comparisons. We distill the priors into a lightweight placement model that achieves the best accuracy among dedicated baselines and zero-shot vision-language models.
作者备注：Accepted to WACV27
观测到的版本
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v2 · 2026-11-05 08:13 · 最新观测版本
HiddenObjects: Scalable Spatial Prior Extraction for Object Placement from Diffusion Models
We learn explicit, class-conditioned spatial priors for object placement in natural scenes by distilling placement knowledge from text-conditioned diffusion models. Prior methods rely on sparse manual annotations or on inpainting object-removal pipelines, whose artifacts promote shortcut learning. To address these limitations, we propose a scalable pipeline that evaluates dense candidate placements by inserting objects into clean backgrounds. This produces HiddenObjects, a dataset of 27M bounding-box annotations (2M positives) across 27K diverse scenes and object categories. Human evaluation prefers our image editing composites over those guided by sparse annotations in 65.6% of comparisons. We distill the priors into a lightweight placement model that achieves the best accuracy among dedicated baselines and zero-shot vision-language models.
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