AesFA: An Aesthetic Feature-Aware Arbitrary Neural Style Transfer

1 code implementation • 10 Dec 2023 • Joonwoo Kwon, Sooyoung Kim, Yuewei Lin, Shinjae Yoo, Jiook Cha

The primary idea is to decompose the image via its frequencies to better disentangle aesthetic styles from the reference image while training the entire model in an end-to-end manner to exclude pre-trained models at inference completely.

Disentanglement Style Transfer