[Submitted on 17 Oct 2026]
Title:Controllable Multi-Object Insertion for Detection Data Augmentation
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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 localization of multiple objects of specific category and count. We design SceneP2F, a two-stage trainable framework that connects scene-aware joint planning with layout-aligned generation leveraging explicit instance-level layouts. SceneP2F consists of two novel designs. The first is ScenePlan which allocates one class-labeled query per requested instance, jointly predicts scene-compatible object centers, and separately estimates box sizes conditioned on the planned centers and scene context. The second is SceneAlign-Fill which combines geometry- and appearance-aware conditioning, box--class-guided regional attention, and an annotation-region-weighted flow-matching objective to render the planned objects while limiting unintended changes to source content. Extensive experiments on three challenging benchmarks UAVDT, VisDrone, and BDD100K show that SceneP2F outperforms representative general-purpose editors and object-insertion methods consistently, demonstrating the value of coordinating instance-level planning and generation for category-targeted detection augmentation.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.26922 [cs.CV]
(or arXiv:2610.26922v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.26922
arXiv-issued DOI via DataCite
Submission history
From: Jiyang Huang [view email] [v1] Sat, 17 Oct 2026 08:23:25 UTC (28,648 KB)
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