As a special case of common object removal, image person removal is playing an increasingly important role in social media and criminal investigation domains. Due to the integrity of person area and the complexity of human posture, person removal has its own dilemmas. In this paper, we propose a novel idea to tackle these problems from the perspective of data synthesis. Concerning the lack of dedicated dataset for image person removal, two dataset production methods are proposed to automatically generate images, masks and ground truths respectively. Then, a learning framework similar to local image degradation is proposed so that the masks can be used to guide the feature extraction process and more texture information can be gathered for final prediction. A coarse-to-fine training strategy is further applied to refine the details. The data synthesis and learning framework combine well with each other. Experimental results verify the effectiveness of our method quantitatively and qualitatively, and the trained network proves to have good generalization ability either on real or synthetic images.
翻译:作为常见物体清除的一个特殊案例,图像清除在社交媒体和刑事调查领域正在发挥越来越重要的作用。由于个人区域的完整和人类态势的复杂性,个人清除有其自身的困境。在本文件中,我们提出了一个从数据综合角度解决这些问题的新想法。关于缺乏用于图像清除的专门数据集,提出了两种数据集制作方法,分别自动生成图像、面具和地面真相。然后,提出了类似于当地图像退化的学习框架,以便利用面具来指导特征提取过程,并收集更多的纹理信息进行最终预测。进一步运用了粗略至细微的培训战略来完善细节。数据综合和学习框架相互配合,实验结果从数量和质量上验证了我们方法的有效性,经过培训的网络证明在真实或合成图像上都具有很好的概括能力。