HiFi-Inpaint：实现高保真参考引导修复技术，生成细节保留的人机交互图像

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

March 2, 2026

作者: Yichen Liu, Donghao Zhou, Jie Wang, Xin Gao, Guisheng Liu, Jiatong Li, Quanwei Zhang, Qiang Lyu, Lanqing Guo, Shilei Wen, Weiqiang Wang, Pheng-Ann Heng

cs.AI

摘要

人-物交互图像作为展现人与产品融合关系的视觉载体，在广告、电商及数字营销领域具有重要作用。此类图像生成的核心挑战在于确保产品细节的高保真还原。现有方法中，基于参考图的修复技术通过利用产品参考图像指导修复过程，提供了针对性解决方案，但仍在三个关键层面存在局限：缺乏多样化的大规模训练数据、现有模型难以聚焦产品细节保留，以及粗粒度监督无法实现精准引导。为解决这些问题，我们提出HiFi-Inpaint——一种专为人-物图像生成设计的新型高保真参考修复框架。该框架通过共享增强注意力（SEA）模块优化细粒度产品特征，并采用基于高频图谱的细节感知损失（DAL）实现像素级精准监督。此外，我们构建了包含4万样本的HP-Image-40K数据集，其样本通过自动筛选流程从合成数据中精选而得。实验结果表明，HiFi-Inpaint能够生成细节保留度极高的人-物交互图像，在各项指标上达到业界最优水平。

English

Human-product images, which showcase the integration of humans and products, play a vital role in advertising, e-commerce, and digital marketing. The essential challenge of generating such images lies in ensuring the high-fidelity preservation of product details. Among existing paradigms, reference-based inpainting offers a targeted solution by leveraging product reference images to guide the inpainting process. However, limitations remain in three key aspects: the lack of diverse large-scale training data, the struggle of current models to focus on product detail preservation, and the inability of coarse supervision for achieving precise guidance. To address these issues, we propose HiFi-Inpaint, a novel high-fidelity reference-based inpainting framework tailored for generating human-product images. HiFi-Inpaint introduces Shared Enhancement Attention (SEA) to refine fine-grained product features and Detail-Aware Loss (DAL) to enforce precise pixel-level supervision using high-frequency maps. Additionally, we construct a new dataset, HP-Image-40K, with samples curated from self-synthesis data and processed with automatic filtering. Experimental results show that HiFi-Inpaint achieves state-of-the-art performance, delivering detail-preserving human-product images.