Hair　is　the　most　important　but　onerous　step　for　depicting　dynamic　3D　virtual　characters.　The　photorealistic　hair　animation　requires　high-quality　simulation　and　rendering　models.　These　models　are　based　on　complex　calculations　of　mechanics　and　optics.　Because　of　the　huge　time　budget,　it　is　difficult　to　apply　in　the　interactive　scene.　A　promising　solution　to　overcome　the　time　budget　is　the　reduced　model　that　struggles　to　reduce　the　computation　of　physical　details　by　various　interpolation　methods.　However,　current　reduced　models　compromise　too　much　reality.　This　research　intends　to　achieve　photorealistic　hair　animation　in　a　fast　way.　Building　a　deep　learning-based　framework　to　synthesize　photorealistic　hair　is　aimed　at.　Furthermore,　this　research　also　presents　a　pipeline　for　hair　merging　into　the　scene.　This　new　framework　enables　the　model　to　significantly　improve　the　appearances　of　hair　animation　while　adding　little　computation　overhead.