Fine-grained　instance　segmentation　is　considerably　more　complicated　and　challenging　than　semantic　segmentation.　Most　existing　instance　segmentation　methods　only　focus　on　accuracy　without　paying　much　attention　to　inference　latency,　which,　is　critical　to　real-time　applications,　such　as　autonomous　driving.　In　this　paper,　we　aim　to　bridge　the　gap　between　semantic　segmentation　and　instance　segmentation　by　presenting　a　novel　real-time　model　for　instance　segmentation,　Sem2Ins,　which　effectively　generates　instance　boundaries　according　to　a　semantic　segmentation　by　leveraging　conditional　generative　adversarial　networks　(cGANs)　coupled　with　deep　supervision　and　a　weighted　fusion　layer.　Specifically,　supervision　is　imposed　on　each　output　layer,　and　features　from　different　levels　are　fused　to　produce　a　well-generated　boundary　map.　Sem2Ins　has　the　following　desirable　features:　1)　Combined　with　some　fast　semantic　segmentation　methods,　Sem2Ins　runs　at　a　real-time　speed　that　is　fairly　well-balanced　against　accuracy;　2)　Sem2Ins　works　flexibly　with　any　semantic　segmentation　model　for　instance　segmentation,　and　if　the　given　semantic　segmentation　is　sufficiently　good,　Sem2Ins　even　achieves　state-of-the-art　in　terms　of　accuracy;　3)　deep　supervision　and　weighted　fusion　can　be　leveraged　to　generate　high-quality　boundaries;　and　4)　Sem2Ins　can　be　easily　extended　to　panoptic　segmentation.　Extensive　experiments　performed　on　the　Cityscapes,　WildDash,　KITTI　and　COCO　benchmarks　have　demonstrated　that　1)　Sem2Ins,　when　combined　with　PSPNet　and　DDRNet-23-Slim,　consistently　outperforms　the　state-of-the-art　real-time　solution　(Box2Pix)　in　terms　of　both　speed　and　accuracy;　and　2)　Sem2Ins　combined　with　DPC　performs　comparably　to　some　powerful　detect-and-segment　approaches.