Text-Guided Portrait Image Matting

Yong Xu, Xin Yao, Baoling Liu, Yuhui Quan, Hui Ji

Abstract: Image matting is a technique used to separate the foreground of an image from the background, which estimates an alpha matte that indicates pixel-wise degree of transparency. To precisely extract target objects and address the ambiguity of solutions in image matting, many existing approaches employ a trimap or background image provided by the user as additional input to guide the matting process. This article introduces a novel matting paradigm termed text-guided image matting, utilizing a textual description of the foreground object as a guiding element. In contrast to trimap or background-based methods, text-guided matting offers a user-friendly interface, providing semantic clues for the objects of interest. Moreover, it facilitates batch processing across multiple frames featuring the same objects of interest. The proposed text-guided matting approach is implemented through a deep neural network (NN) comprising three-stage cross-modal feature fusion and two-step alpha matte prediction. Experimental results on portrait matting demonstrate the competitive performance of our text-guided approach compared to existing trimap-based and background-based methods.