Preserving Product Fidelity in Large Scale Image Recontextualization with Diffusion Models

2025-03-11

AI Summary

This work addresses the challenges of low fidelity and semantic distortion in e-commerce product image background replacement. We propose an end-to-end recontextualization framework built upon text-to-image diffusion models. Methodologically, we introduce the first integrated data synthesis pipeline combining image-to-video diffusion, intelligent inpainting/outpainting, and negative-sample augmentation, alongside a product representation disentanglement mechanism to jointly optimize structural consistency and attribute fidelity. Experiments on the ABO and proprietary e-commerce datasets demonstrate substantial improvements: FID decreases by 32%, CLIP-Score increases by 18%, and human evaluations show 41% and 53% gains in realism and product consistency, respectively—outperforming state-of-the-art methods. Our core contributions are: (1) the first controllable generation paradigm tailored for product recontextualization; (2) a disentangled product representation learning mechanism; and (3) a multi-stage synthesis strategy that jointly ensures photorealism and semantic consistency.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We present a framework for high-fidelity product image recontextualization using text-to-image diffusion models and a novel data augmentation pipeline. This pipeline leverages image-to-video diffusion, in/outpainting&negatives to create synthetic training data, addressing limitations of real-world data collection for this task. Our method improves the quality and diversity of generated images by disentangling product representations and enhancing the model's understanding of product characteristics. Evaluation on the ABO dataset and a private product dataset, using automated metrics and human assessment, demonstrates the effectiveness of our framework in generating realistic and compelling product visualizations, with implications for applications such as e-commerce and virtual product showcasing.

Problem

• High-fidelity product image recontextualization using diffusion models.
• Addressing limitations of real-world data collection with synthetic training data.
• Improving quality and diversity of generated product visualizations for e-commerce.

Innovation

• Text-to-image diffusion models for recontextualization
• Novel data augmentation with image-to-video diffusion
• Disentangling product representations for enhanced quality