HiFi-Inpaint：實現高保真度參考式修復 生成細節保留的人機產品圖像

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

人與產品互動圖像作為展現人與產品融合的重要媒介，在廣告、電子商務和數位行銷領域具有關鍵作用。生成此類圖像的核心挑戰在於確保產品細節的高保真度還原。在現有範式中，基於參考圖像的修復技術通過利用產品參考圖來引導修復過程，提供了針對性解決方案，但仍在三個關鍵方面存在侷限性：缺乏多樣化的大規模訓練數據、現有模型難以專注於產品細節保留，以及粗粒度監督無法實現精確引導。為解決這些問題，我們提出HiFi-Inpaint——專為生成人與產品互動圖像設計的新型高保真參考修復框架。該框架引入共享增強注意力機制來優化細粒度產品特徵，並採用基於高頻圖譜的細節感知損失函數實現像素級精確監督。此外，我們構建了包含4萬個樣本的新數據集HP-Image-40K，其中樣本通過自動化過濾流程從自合成數據中篩選生成。實驗結果表明，HiFi-Inpaint能實現細節保留度極高的人與產品互動圖像生成，達到當前最先進的性能水平。

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.