Frontal　person　images　contain　the　richest　detailed　features　of　humans,　which　can　effectively　assist　in　behavioral　recognition,　virtual　dress　fitting　and　other　applications.　While　many　remarkable　networks　are　devoted　to　the　person　image　generation　task,　most　of　them　need　accurate　target　poses　as　the　network　inputs.　However,　the　target　pose　annotation　is　difficult　and　time-consuming.　In　this　work,　we　proposed　a　first　frontal　person　image　generation　network　based　on　the　proposed　anchor　pose　set　and　the　generative　adversarial　network.　Specifically,　our　method　first　classify　a　rough　frontal　pose　to　the　input　human　image　based　on　the　proposed　anchor　pose　set,　and　regress　all　key　points　of　the　rough　frontal　pose　to　estimate　an　accurate　frontal　pose.　Then,　we　consider　the　estimated　frontal　pose　as　the　target　pose,　and　construct　a　two-stream　generator　based　on　the　generative　adversarial　network　to　update　the　person＇s　shape　and　appearance　feature　in　a　crossing　way　and　generate　a　realistic　frontal　person　image.　Experiments　on　the　challenging　CMU　Panoptic　dataset　show　that　our　method　can　generate　realistic　frontal　images　from　arbitrary-view　human　images.