Taming diffusion model for exemplar-based image translation
摘要
Exemplar-based image translation involves converting semantic masks into photorealistic images that adopt the style of a given exemplar.However,most existing GAN-based translation methods fail to produce photorealistic results.In this study,we propose a new diffusion model-based approach for generating high-quality images that are semantically aligned with the input mask and resemble an exemplar in style.The proposed method trains a conditional denoising diffusion probabilistic model(DDPM)with a SPADE module to integrate the semantic map.We then used a novel contextual loss and auxiliary color loss to guide the optimization process,resulting in images that were visually pleasing and semantically accurate.Experiments demonstrate that our method outperforms state-of-the-art approaches in terms of both visual quality and quantitative metrics.
Author：
Hao Ma
Jingyuan Yang
Hui Huang
作者单位：
Visual Computing Research Center,College of Com-puter Science and Software Engineering,Shenzhen University,Shenzhen 518060,China
年，卷(期)：
2024, 10(6)
机标分类号：
TQ0
TP311.52
TH137.52
在线出版日期：
2025年4月23日
