I-Matting: Improved Trimap-Free Image Matting

Zichuan Liu, Ke Wang, Mingyuan Wu, Lantao Yu, Klara Nahrstedt, Xin Lu

Abstract: Image matting has become an essential functionality of image capturing and editing tools. While trimap and scribble-based techniques have shown notable success in these applications, generating high-quality alpha mattes without trimap inputs remains challenging. Existing trimap-free methods divide the task into coarse semantic mask prediction and detailed matte prediction, and an optimization is formulated by balancing these two tasks. However, emphasizing the optimization of the coarse mask leads to inaccurate matte, and emphasizing the optimization of the detailed matte leads to degraded semantic integrity or background artifacts. In this paper, we propose an improved trimap-free training strategy (I-Matting) that effectively ensures semantic integrity, removes background artifacts, and improves local details. First, we introduce two discriminators to distinguish the matting outputs versus the ground truths, which boosts the semantic without hurting the matte prediction. Second, a novel patch-rank module is proposed to improve the matting accuracy by leveraging high-resolution inputs, without hurting the semantic integrity. Meanwhile, the accuracy gain produced by I-Matting is not at the expense of any additional cost in the inference. Extensive experiments show that our method significantly outperforms existing approaches.