HiFi-Inpaint: Towards High-Fidelity Reference-Based Inpainting for Generating Detail-Preserving Human-Product Images

2026-03-02 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Advanced, quick

Summary

HiFi-Inpaint is a new high-fidelity reference-based inpainting framework designed for generating human-product images, crucial for advertising and e-commerce. It addresses limitations in existing methods, specifically the lack of diverse large-scale training data, models' inability to focus on product detail preservation, and imprecise guidance from coarse supervision. The framework introduces Shared Enhancement Attention (SEA) to refine fine-grained product features and Detail-Aware Loss (DAL) for precise pixel-level supervision using high-frequency maps. Additionally, its development included the creation of a new dataset, HP-Image-40K, curated from self-synthesis data and processed with automatic filtering. Experimental results indicate that HiFi-Inpaint achieves state-of-the-art performance in generating detail-preserving human-product images.

Key takeaway

For Computer Vision Engineers developing advertising or e-commerce image generation tools, HiFi-Inpaint offers a significant advancement in preserving product details. You should consider integrating its SEA and DAL mechanisms to enhance the fidelity of human-product images, especially when precise detail is critical for marketing effectiveness. Explore the HP-Image-40K dataset for training your models.

Key insights

HiFi-Inpaint improves human-product image generation by enhancing detail preservation through novel attention and loss mechanisms.

Principles

• Reference images guide inpainting.
• Fine-grained features require specific attention.
• Pixel-level supervision improves detail.

Method

HiFi-Inpaint uses Shared Enhancement Attention (SEA) for feature refinement and Detail-Aware Loss (DAL) with high-frequency maps for precise pixel-level supervision in reference-based inpainting.

In practice

• Generate high-fidelity human-product images.
• Improve product detail in advertising visuals.
• Utilize HP-Image-40K for training.

Topics

• Reference-Based Inpainting
• Human-Product Image Generation
• Detail Preservation
• Shared Enhancement Attention
• HP-Image-40K Dataset

Best for: Computer Vision Engineer, Research Scientist, AI Researcher, AI Scientist, Deep Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.