HiddenObjects: Scalable Diffusion-Distilled Spatial Priors for Object Placement

ArXi:2604.10675v1 Announce Type: new We propose a method to learn explicit, class-conditioned spatial priors for object placement in natural scenes by distilling the implicit placement knowledge encoded in text-conditioned diffusion models. Prior work relies either on manually annotated data, which is inherently limited in scale, or on inpainting-based object-removal pipelines, whose artifacts promote shortcut learning. To address these limitations, we