@article{Ma2024, 
author = {Hao Ma and Jingyuan Yang and Hui Huang},
title = {Taming diffusion model for exemplar-based image translation},
year = {2024},
journal = {Computational Visual Media},
volume = {10},
number = {6},
pages = {1031-1043},
keywords = {exemplar, image translation, denoising diffusion probabilistic model (DDPM)},
url = {https://www.sciopen.com/article/10.1007/s41095-023-0371-3},
doi = {10.1007/s41095-023-0371-3},
abstract = {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.}
}