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.

翻译：人-物图像展现了人物与产品的融合，在广告、电子商务和数字营销中发挥着至关重要的作用。生成此类图像的核心挑战在于确保产品细节的高保真保留。在现有范式中，基于参考的修复通过利用产品参考图像指导修复过程，提供了一种针对性解决方案。然而，该方法在三个关键方面仍存在局限：缺乏多样化的大规模训练数据、现有模型难以专注于产品细节保留，以及粗粒度监督无法实现精确引导。为解决这些问题，我们提出了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。HiFi-Inpaint引入了共享增强注意力（SEA）以优化细粒度产品特征，并采用细节感知损失（DAL）通过高频图实施精确的像素级监督。此外，我们构建了一个新数据集HP-Image-40K，其样本通过自合成数据筛选并经过自动过滤处理。实验结果表明，HiFi-Inpaint实现了最先进的性能，能够生成细节保持良好的人-物图像。