Automated Virtual Product Placement and Assessment in Images using Diffusion Models
CoRR(2024)
Abstract
In Virtual Product Placement (VPP) applications, the discrete integration of
specific brand products into images or videos has emerged as a challenging yet
important task. This paper introduces a novel three-stage fully automated VPP
system. In the first stage, a language-guided image segmentation model
identifies optimal regions within images for product inpainting. In the second
stage, Stable Diffusion (SD), fine-tuned with a few example product images, is
used to inpaint the product into the previously identified candidate regions.
The final stage introduces an "Alignment Module", which is designed to
effectively sieve out low-quality images. Comprehensive experiments demonstrate
that the Alignment Module ensures the presence of the intended product in every
generated image and enhances the average quality of images by 35
presented in this paper demonstrate the effectiveness of the proposed VPP
system, which holds significant potential for transforming the landscape of
virtual advertising and marketing strategies.
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