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POCI-Diff framework generates synthetic surveillance data with 3D control

By PulseAugur Editorial · [1 sources] ·

Researchers have developed POCI-Diff, a novel framework for generating synthetic visual surveillance data. This method allows for fine-grained 3D control over object placement and appearance, addressing limitations in existing synthetic data generation techniques. POCI-Diff integrates Blended Latent Diffusion with depth-conditioned ControlNet to create complex multi-object scenes in a single pass, binding text descriptions to specific 3D locations. The framework also includes an editing pipeline for object insertion, removal, and transformation, maintaining appearance consistency through IP-Adapter. AI

IMPACT Enables more robust and privacy-preserving training of visual surveillance models through controllable synthetic data generation.

RANK_REASON The cluster contains a research paper detailing a new method for synthetic data generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

POCI-Diff framework generates synthetic surveillance data with 3D control

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The cluster contains a research paper detailing a new method for synthetic data generation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Andrea Rigo, Luca Stornaiuolo, Weijie Wang, Mauro Martino, Bruno Lepri, Nicu Sebe ·

    POCI-Diff: 3D-Layout Guided Diffusion for Controllable Synthetic Surveillance Data Generation

    arXiv:2601.14056v2 Announce Type: replace-cross Abstract: Training robust visual surveillance models requires large-scale datasets with precise spatial annotations, yet collecting real surveillance data is costly, privacy-sensitive, and often legally constrained. Synthetic data g…

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