A High Performance CRF Model for Clothes Parsing

Abstract. In this paper we tackle the problem of clothing parsing: Our goal is to segment and classify different garments a person is wearing. We frame the prob-lem as the one of inference in a pose-aware Conditional Random Field (CRF) which exploits appearance, figure/ground segmentation, shape and location pri-ors for each garment as well as similarities between segments, and symmetries between different human body parts. We demonstrate the effectiveness of our ap-proach on the Fashionista dataset [1] and show that we can obtain a significant improvement over the state-of-the-art.