Automated Virtual Product Placement and Assessment in Images using Diffusion Models

• URL: http://arxiv.org/abs/2405.01130v1
• Date: Thu, 2 May 2024 09:44:13 GMT
• Title: Automated Virtual Product Placement and Assessment in Images using Diffusion Models
• Authors: Mohammad Mahmudul Alam, Negin Sokhandan, Emmett Goodman,
• Abstract summary: 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.
• Score: 1.63075356372232
• License: http://creativecommons.org/licenses/by/4.0/
• 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%. 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.

Related papers

• PixelWorld: Towards Perceiving Everything as Pixels [50.13953243722129]
  We propose to unify all modalities (text, tables, code, diagrams, images, etc) as pixel inputs, i.e. "Perceive Everything as Pixels" (PEAP) We introduce PixelWorld, a novel evaluation suite that unifies all the mentioned modalities into pixel space to gauge the existing models' performance.
  arXiv  Detail & Related papers  (2025-01-31T17:39:21Z)
• An Evaluation Framework for Product Images Background Inpainting based on Human Feedback and Product Consistency [4.177224329586615]
  In product advertising applications, the automated inpainting of backgrounds utilizing AI techniques in product images has emerged as a significant task. Human Feedback and Product Consistency (HFPC) can automatically assess the generated product images based on two modules. HFPC achieves state-of-the-art(96.4% in precision) in comparison to other open-source visual-quality-assessment models.
  arXiv  Detail & Related papers  (2024-12-23T12:03:35Z)
• Flex3D: Feed-Forward 3D Generation With Flexible Reconstruction Model And Input View Curation [61.040832373015014]
  We propose Flex3D, a novel framework for generating high-quality 3D content from text, single images, or sparse view images. We employ a fine-tuned multi-view image diffusion model and a video diffusion model to generate a pool of candidate views, enabling a rich representation of the target 3D object. In the second stage, the curated views are fed into a Flexible Reconstruction Model (FlexRM), built upon a transformer architecture that can effectively process an arbitrary number of inputs.
  arXiv  Detail & Related papers  (2024-10-01T17:29:43Z)
• SpotActor: Training-Free Layout-Controlled Consistent Image Generation [43.2870588035256]
  We present a new formalization of dual energy guidance with optimization in a dual semantic-latent space. We propose a training-free pipeline, SpotActor, which features a layout-conditioned backward update stage and a consistent forward sampling stage. The results prove that SpotActor fulfills the expectations of this task and showcases the potential for practical applications.
  arXiv  Detail & Related papers  (2024-09-07T11:52:48Z)
• OrientDream: Streamlining Text-to-3D Generation with Explicit Orientation Control [66.03885917320189]
  OrientDream is a camera orientation conditioned framework for efficient and multi-view consistent 3D generation from textual prompts. Our strategy emphasizes the implementation of an explicit camera orientation conditioned feature in the pre-training of a 2D text-to-image diffusion module. Our experiments reveal that our method not only produces high-quality NeRF models with consistent multi-view properties but also achieves an optimization speed significantly greater than existing methods.
  arXiv  Detail & Related papers  (2024-06-14T13:16:18Z)
• Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent Diffusion [50.59261592343479]
  We present Kandinsky1, a novel exploration of latent diffusion architecture. The proposed model is trained separately to map text embeddings to image embeddings of CLIP. We also deployed a user-friendly demo system that supports diverse generative modes such as text-to-image generation, image fusion, text and image fusion, image variations generation, and text-guided inpainting/outpainting.
  arXiv  Detail & Related papers  (2023-10-05T12:29:41Z)
• Guide3D: Create 3D Avatars from Text and Image Guidance [55.71306021041785]
  Guide3D is a text-and-image-guided generative model for 3D avatar generation based on diffusion models. Our framework produces topologically and structurally correct geometry and high-resolution textures.
  arXiv  Detail & Related papers  (2023-08-18T17:55:47Z)
• C-VTON: Context-Driven Image-Based Virtual Try-On Network [1.0832844764942349]
  We propose a Context-Driven Virtual Try-On Network (C-VTON) that convincingly transfers selected clothing items to the target subjects. At the core of the C-VTON pipeline are: (i) a geometric matching procedure that efficiently aligns the target clothing with the pose of the person in the input images, and (ii) a powerful image generator that utilizes various types of contextual information when the final try-on result.
  arXiv  Detail & Related papers  (2022-12-08T17:56:34Z)
• VISTA: Vision Transformer enhanced by U-Net and Image Colorfulness Frame Filtration for Automatic Retail Checkout [0.7250756081498245]
  We propose to segment and classify individual frames from a video sequence. The segmentation method consists of a unified single product item- and hand-segmentation followed by entropy masking. Our best system achieves 3rd place in the AI City Challenge 2022 Track 4 with an F1 score of 0.4545.
  arXiv  Detail & Related papers  (2022-04-23T08:54:28Z)
• IMAGINE: Image Synthesis by Image-Guided Model Inversion [79.4691654458141]
  We introduce an inversion based method, denoted as IMAge-Guided model INvErsion (IMAGINE), to generate high-quality and diverse images. We leverage the knowledge of image semantics from a pre-trained classifier to achieve plausible generations. IMAGINE enables the synthesis procedure to simultaneously 1) enforce semantic specificity constraints during the synthesis, 2) produce realistic images without generator training, and 3) give users intuitive control over the generation process.
  arXiv  Detail & Related papers  (2021-04-13T02:00:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.