CatalogStitch: Dimension-Aware and Occlusion-Preserving Object Compositing for Catalog Image Generation

2026-04-10

AI Summary

This work addresses the heavy reliance on manual intervention—such as hand-crafted mask resizing and occlusion inpainting—in existing product catalog image synthesis methods. The authors propose a model-agnostic, end-to-end automated framework that generates high-quality composites from only a product image and a background, leveraging a novel dimension-aware masking algorithm and an occlusion-aware hybrid inpainting mechanism. Key contributions include the dimension-aware masking strategy, the occlusion-aware inpainting approach, the CatalogStitch-Eval benchmark comprising 58 complex real-world scenes, and an accompanying visualization toolkit. Experiments demonstrate consistent and significant improvements in both synthesis quality and efficiency across three representative models—ObjectStitch, OmniPaint, and InsertAnything—without requiring any post-processing.

Technology Category

• Computer Vision: Computational Photography, Image & Video Synthesis
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Generative object compositing methods have shown remarkable ability to seamlessly insert objects into scenes. However, when applied to real-world catalog image generation, these methods require tedious manual intervention: users must carefully adjust masks when product dimensions differ, and painstakingly restore occluded elements post-generation. We present CatalogStitch, a set of model-agnostic techniques that automate these corrections, enabling user-friendly content creation. Our dimension-aware mask computation algorithm automatically adapts the target region to accommodate products with different dimensions; users simply provide a product image and background, without manual mask adjustments. Our occlusion-aware hybrid restoration method guarantees pixel-perfect preservation of occluding elements, eliminating post-editing workflows. We additionally introduce CatalogStitch-Eval, a 58-example benchmark covering aspect-ratio mismatch and occlusion-heavy catalog scenarios, together with supplementary PDF and HTML viewers. We evaluate our techniques with three state-of-the-art compositing models (ObjectStitch, OmniPaint, and InsertAnything), demonstrating consistent improvements across diverse catalog scenarios. By reducing manual intervention and automating tedious corrections, our approach transforms generative compositing into a practical, human-friendly tool for production catalog workflows.

Problem

• object compositing
• catalog image generation
• dimension mismatch
• occlusion preservation
• manual intervention

Innovation

• dimension-aware compositing
• occlusion-preserving restoration
• catalog image generation
• model-agnostic object insertion
• automated mask adaptation