Towards Natural Image Matting in the Wild via Real-Scenario Prior
Authors: name: Ruihao Xia, name: Yu Liang, name: Peng-Tao Jiang, name: Hao Zhang, name: Qianru Sun, name: Yang Tang, name: Bo Li, name: Pan Zhou

Recent approaches attempt to adapt powerful interactive segmentation models,
such as SAM, to interactive matting and fine-tune the models based on synthetic
matting datasets. However, models trained on synthetic data fail to generalize
to complex and occlusion scenes. We address this challenge by proposing a new
matting dataset based on the COCO dataset, namely COCO-Matting. Specifically,
the construction of our COCO-Matting includes accessory fusion and
mask-to-matte, which selects real-world complex images from COCO and converts
semantic segmentation masks to matting labels. The built COCO-Matting comprises
an extensive collection of 38,251 human instance-level alpha mattes in complex
natural scenarios. Furthermore, existing SAM-based matting methods extract
intermediate features and masks from a frozen SAM and only train a lightweight
matting decoder by end-to-end matting losses, which do not fully exploit the
potential of the pre-trained SAM. Thus, we propose SEMat which revamps the
network architecture and training objectives. For network architecture, the
proposed feature-aligned transformer learns to extract fine-grained edge and
transparency features. The proposed matte-aligned decoder aims to segment
matting-specific objects and convert coarse masks into high-precision mattes.
For training objectives, the proposed regularization and trimap loss aim to
retain the prior from the pre-trained model and push the matting logits
extracted from the mask decoder to contain trimap-based semantic information.
Extensive experiments across seven diverse datasets demonstrate the superior
performance of our method, proving its efficacy in interactive natural image
matting. We open-source our code, models, and dataset at
https://github.com/XiaRho/SEMat.