arXiv:2610.26922v1 Announce Type: new
Abstract: Controllable object insertion provides a targeted approach to augmenting object detection datasets that require category–count fidelity, annotation alignment, and source-content preservation beyond visual realism. Several approaches have been proposed to address this grand challenge, but most suffer from different constraints in precise category–count requests and visual consistency, limited coordination across categories and instances, and poor