HiFi-Inpaint: Towards High-Fidelity Reference-Based Inpainting for Generating Detail-Preserving Human-Product Images
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.
69a686fd663a122a7ce66893
Yichen Liu
69a686fd663a122a7ce66894
Donghao Zhou
claimed_verified
2026-03-04T09:24:31.306Z
67136093d2e50f1e8c9fad52
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/0q49MyGuav8lJ9CIeyLhu.png
Donghao Zhou
donghao-zhou
user
donghao-zhou
69a686fd663a122a7ce66895
Jie Wang
69a686fd663a122a7ce66896
Xin Gao
69a686fd663a122a7ce66897
Guisheng Liu
69a686fd663a122a7ce66898
Jiatong Li
69a686fd663a122a7ce66899
Quanwei Zhang
69a686fd663a122a7ce6689a
Qiang Lyu
69a686fd663a122a7ce6689b
Lanqing Guo
69a686fd663a122a7ce6689c
Shilei Wen
69a686fd663a122a7ce6689d
Weiqiang Wang
69a686fd663a122a7ce6689e
Pheng-Ann Heng
HiFi-Inpaint: Towards High-Fidelity Reference-Based Inpainting for Generating Detail-Preserving Human-Product Images
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.
HiFi-Inpaint generates high-fidelity human-product images using shared enhancement attention and detail-aware loss with a new 40K-image dataset.
reference-based inpainting high-fidelity preservation Shared Enhancement Attention Detail-Aware Loss high-frequency maps human-product images