We present a novel deep learning based image inpainting system to complete images with free-form masks and inputs. The system is based on gated convolutions learned from millions of images without additional labelling efforts. The proposed gated convolution solves the issue of vanilla convolution that treats all input pixels as valid ones, generalizes partial convolution by providing a learnable dynamic feature selection mechanism for each channel at each spatial location across all layers. Moreover, as free-form masks may appear anywhere in images with any shapes, global and local GANs designed for a single rectangular mask are not suitable. To this end, we also present a novel GAN loss, named SN-PatchGAN, by applying spectral-normalized discriminators on dense image patches. It is simple in formulation, fast and stable in training. Results on automatic image inpainting and user-guided extension demonstrate that our system generates higher-quality and more flexible results than previous methods. We show that our system helps users quickly remove distracting objects, modify image layouts, clear watermarks, edit faces and interactively create novel objects in images. Furthermore, visualization of learned feature representations reveals the effectiveness of gated convolution and provides an interpretation of how the proposed neural network fills in missing regions. More high-resolution results and video materials are available at http://jiahuiyu.com/deepfill2

本文提出了一种建基于门控卷积的生成式图像修复系统，用于完成自由形状掩码和引导的图片修复任务。所提出的门控卷积解决了通用卷积中将所有输入像素视为有效像素的问题，并提供了一种可学习的动态特征选择机制，以适应于每个通道和空间位置上的输入。作者还提出了一种名为 SN-PatchGAN 的基于图像块的 GAN 损失，可以应用于自由形状掩码和根据用户指导进行图像扩展，并证明所提出的方法比以往任何方法都更加高质量和灵活性。

门控卷积自由形式图像修复