论文标题
通过数据综合的掩模引导的图像人员去除
Mask-Guided Image Person Removal with Data Synthesis
论文作者
论文摘要
作为删除对象的特殊情况，删除图像人员在社交媒体和刑事调查领域中起着越来越重要的作用。由于人区域的完整性和人类姿势的复杂性，人们的遣散有其自身的困境。在本文中，我们提出了一个新颖的想法，以从数据综合的角度解决这些问题。关于缺少用于删除图像人员的专用数据集，提出了两种数据集生产方法，分别自动生成图像，掩蔽和地面真相。然后，提出了类似于本地图像退化的学习框架，以便可以使用掩码来指导特征提取过程，并可以收集更多纹理信息以进行最终预测。进一步采用粗线到细节的培训策略来完善细节。数据综合和学习框架相互结合。实验结果在定量和质量上验证了我们方法的有效性，并且训练有素的网络在实际图像或合成图像上具有良好的概括能力。
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.
