Spatial-Separated Curve Rendering Network for Efficient and High-Resolution Image Harmonization

Code

• github.com/stefanleong/s2crnet
  OfficialIn paperpytorch★ 31
• github.com/vinthony/s2crnet-demos
  pytorch★ 5

Abstract

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^2CRNet) for efficient and high-resolution image harmonization for the first time. In S^2CRNet, 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., 20482048) in 0.1 seconds with much lower GPU computational resources than all existing methods. The code will be made available at http://github.com/stefanLeong/S2CRNet.

Tasks

Reproductions