Abstract
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.
Abstract (translated)
人与产品图像展示了人类和产品的结合，在广告、电子商务和数字营销中扮演着重要角色。生成这类图片的关键挑战在于确保高保真地保留产品细节。现有方法中，基于参考的修复（inpainting）提供了一种通过利用产品参考图来引导修复过程的目标解决方案。然而，这一方法在三个方面仍存在局限：缺乏多样化的大规模训练数据、现有模型难以专注于保持产品细节以及粗略监督无法实现精确指导。 为了解决这些问题，我们提出了HiFi-Inpaint，这是一个新型的高保真度基于参考的修补框架，专用于生成人与产品图像。HiFi-Inpaint 引入了共享增强注意力（SEA），以精炼细粒度的产品特征，并引入细节感知损失（DAL）来使用高频图强制执行精确的像素级监督。 此外，我们构建了一个新的数据集HP-Image-40K，该数据集包含从自我合成的数据中挑选出来的样本并经过自动过滤处理。实验结果表明，HiFi-Inpaint 达到了最先进的性能水平，并能够生成保持细节的人与产品图像。
URL
https://arxiv.org/abs/2603.02210
