We show qualitative comparison w.r.t. Background substraction, Semantic segmentation (Deeplabv3+) and Alpha matting techniques. For Alpha matting algorithms, we compare with state-of-the-art (i) trimap based methods Context Aware Matting (CAM) and Index Matting (IM), where trimap is automatically created from segmentation, and (ii) automatic matting algorithm Late Fusion Matting (LFM). Our algorithm is first trained on synthetic-composite Adobe dataset with supervision ( Ours Adobe
) and then on unlabelled real data with self-supervision and adversarial loss ( Ours Real
). We also show that trianing on real data improves matting quality.
