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%. The results
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.

介绍了一种新颖的三阶段全自动虚拟产品放置系统，通过语言引导图像分割模型识别出图像中适合进行产品修复的区域，并使用经过微调的 Stable Diffusion 算法将产品修复到先前识别出的候选区域中，最后引入一个 “对齐模块” 有效地筛选出质量低的图像。综合实验表明，该对齐模块确保每个生成的图像中都存在所需的产品，并提高了图像的平均质量 35%。本文提出的虚拟产品放置系统展示了显著的潜力，可用于改变虚拟广告和营销策略的领域。

使用扩散模型在图像中自动进行虚拟产品放置和评估

Automated Virtual Product Placement and Assessment in Images using  Diffusion Models

Virtual Product placement(VPP) is the advertising technique of digitally
placing a branded object into the scene of a movie or TV show. This type of
advertising provides the ability for brands to reach consumers without
interrupting the viewing experience with a commercial break, as the products
are seen in the background or as props. Despite this being a billion-dollar
industry, ad rendering technique is currently executed at post production
stage, manually either with the help of VFx artists or through semi-automated
solutions. In this paper, we demonstrate a fully automated framework to
digitally place 2-D ads in linear TV cooking shows captured using single-view
camera with small camera movements. Without access to full video or production
camera configuration, this framework performs the following tasks (i)
identifying empty space for 2-D ad placement (ii) kitchen scene understanding
(iii) occlusion handling (iv) ambient lighting and (v) ad tracking.

该研究论文介绍了一种完全自动的框架，可在单视角摄录制的线性电视烹饪展示中，识别空白区域，进行框内广告放置，厨房场景理解、遮挡处理、环境光照和广告跟踪等多种大数据任务，实现了产品虚拟放置技术的自动化。