In the portrait matting, the goal is to predict an alpha matte that identifies the effect of each pixel on the foreground subject. Traditional approaches and most of the existing works utilized an additional input, e.g., trimap, background image, to predict alpha matte. However, (1) providing additional input is not always practical, and (2) models are too sensitive to these additional inputs. To address these points, in this paper, we introduce an additional input-free approach to perform portrait matting. We divide the task into two subtasks, segmentation and alpha matte prediction. We first generate a coarse segmentation map from the input image and then predict the alpha matte by utilizing the image and segmentation map. Besides, we present a segmentation encoding block to downsample the coarse segmentation map and provide useful feature representation to the residual block, since using a single encoder causes the vanishing of the segmentation information. We tested our model on four different benchmark datasets. The proposed method outperformed the MODNet and MGMatting methods that also take a single input. Besides, we obtained comparable results with BGM-V2 and FBA methods that require additional input.
翻译:在肖像垫位中,目标是预测一个阿尔法垫子,确定每个像素对前景主题的影响。传统的方法和大部分现有作品利用了额外的投入,例如三角图、背景图像,来预测阿尔法垫子。然而,(1) 提供额外投入并非始终是实用的,(2) 模型对这些额外投入太敏感。为了解决这些点,我们在本文件中引入了额外的无投入化方法来进行肖像垫子。我们将任务分为两个子任务、分区和α垫子预测。我们首先从输入图像中绘制粗略的分割图,然后利用图像和分割图来预测阿尔法垫子。此外,我们提出了一个分解编码区块,以冲淡毛分割图,并为剩余区块提供有用的特征代表,因为使用单个编码器导致分离信息消失。我们用四个不同的基准数据集测试了我们的模型。拟议方法比MODNet和MGMatting方法高得多,这些方法也采用了单一的输入。此外,我们用BGMV2和FBA的额外输入方法取得了可比的结果。