Attentional Wavelet Network for Traditional Chinese Painting Transfer
Rui Wang, Huaibo Huang, Aihua Zheng, Ran He · 2021
Traditional Chinese paintings pay more attention to `Gongbi' and `Xieyi' in artworks, which raises a challenging task to generate Chinese paintings from photos. `Xieyi' creates high-level conception for paintings, while `Gongbi' refers to portraying local details in paintings. This paper proposes an attentional wavelet network for photo to Chinese painting transferring. We first introduce wavelets to obtain high-level conception and local details in Chinese paintings via 2-D haar wavelet transform. Moreover, we design high-level transform stream and local enhancement stream to dispose high frequencies and low frequency respectively. Furthermore, we exploit self-attention mechanism to compatibly pick up high-level information which is used to remedy the missing details when reconstructing the Chinese painting. To advance our experiment, we set up a new dataset named P2ADataset, with diverse photos and Chinese paintings on famous mountains around China. Experimental results comparing with the state-of-the-art style transferring algorithms verify the effectiveness of the proposed method. We will release the codes and data to the public.