Toward Intelligent Scene Augmentation for Context-Aware Object Placement and Sponsor-Logo Integration

TL;DR

This paper introduces novel tasks and datasets for context-aware object insertion and sponsor-logo augmentation in images, leveraging advances in vision-language and generative models to improve visual editing realism and brand integration.

Contribution

It proposes two new tasks and creates datasets for context-aware object placement and sponsor-logo augmentation, addressing limitations of existing visual editing methods.

Findings

Developed datasets with annotations for new tasks

Demonstrated improved plausibility in object placement

Enhanced brand logo integration in images

Abstract

Intelligent image editing increasingly relies on advances in computer vision, multimodal reasoning, and generative modeling. While vision-language models (VLMs) and diffusion models enable guided visual manipulation, existing work rarely ensures that inserted objects are \emph{contextually appropriate}. We introduce two new tasks for advertising and digital media: (1) \emph{context-aware object insertion}, which requires predicting suitable object categories, generating them, and placing them plausibly within the scene; and (2) \emph{sponsor-product logo augmentation}, which involves detecting products and inserting correct brand logos, even when items are unbranded or incorrectly branded. To support these tasks, we build two new datasets with category annotations, placement regions, and sponsor-product labels.