We　present　a　facial　animation　system　for　ordinary　singlecameral　videos　based　on　2D　shape　regression.　Unlike　some　prior　facial　animation　techniques,　our　system　doesn’t　need　complex　equipment.　The　system　consists　of　firstly　a　Cascade　Multi-Channel　Convolutional　Neural　Network　(CMC-CNN)　model　to　accurately　detect　facial　landmarks　from　2D　video　frames.　Based　on　these　detected　2D　points,　the　facial　motion　parameters,　including　the　head　pose　and　facial　expressions,　are　recovered.　Then　the　system　animates　a　bone-driven　3D　avatar　with　the　facial　motion　parameters.　Experiments　show　that　our　system　can　accurately　detect　facial　landmarks　and　the　animation　results　are　visually　plausible　and　similar　to　the　user’s　facial　motion.　©　Springer　International　Publishing　AG　2016.