  Image harmonization aims to modify the color of the composited region with
respect to the specific background. Previous works model this task as a
pixel-wise image-to-image translation using UNet family structures. However,
the model size and computational cost limit the ability of their models on edge
devices and higher-resolution images. To this end, we propose a novel
spatial-separated curve rendering network(S$^2$CRNet) for efficient and
high-resolution image harmonization for the first time. In S$^2$CRNet, we
firstly extract the spatial-separated embeddings from the thumbnails of the
masked foreground and background individually. Then, we design a curve
rendering module(CRM), which learns and combines the spatial-specific knowledge
using linear layers to generate the parameters of the piece-wise curve mapping
in the foreground region. Finally, we directly render the original
high-resolution images using the learned color curve. Besides, we also make two
extensions of the proposed framework via the Cascaded-CRM and Semantic-CRM for
cascaded refinement and semantic guidance, respectively. Experiments show that
the proposed method reduces more than 90% parameters compared with previous
methods but still achieves the state-of-the-art performance on both synthesized
iHarmony4 and real-world DIH test sets. Moreover, our method can work smoothly
on higher resolution images(eg., $2048\times2048$) in 0.1 seconds with much
lower GPU computational resources than all existing methods. The code will be
made available at \url{http://github.com/stefanLeong/S2CRNet}.
