A processor implemented method for draping a 3D garment on a 3D digital human body, the method comprising: receiving, via one or more hardware processors, a plurality of human body inputs which includes data representation of a subject; creating, using a skinned multi person linear, SMPL, body model, via the one or more hardware processors, a 3D digital human body of the subject based on a body shape, β, and a body pose, θ, from the plurality of human body inputs, wherein the SMPL body model outputs the 3D human body as a function of the body pose and body shape and comprises a linear function T b (β,θ) adding body pose and body shape dependent deformations; predicting, using a multi-layer perceptron model, via the one or more hardware processors, garment vertices offsets, Ô, based on the plurality of human body inputs and garment style coefficients, wherein the garment mesh vertices are defined as a subset of the SMPL model mesh vertices; constructing, using a garment skinning function, via the one or more hardware processors, a 3D skinned garment for the subject using the garment vertices offsets, Ô, wherein the garment skinning function is defined by the equation G ^ = G θ, β, Ô = W T g θ β O ^, J β, θ, w; and Where, W is a garment skinning function, J (.) is a skeleton joint prediction function, - T g θ, β, Ô = IT b β θ + Ô, wherein I is an indicator matrix, whose element i mn indicates that the m th garment vertex is associated with the n th SMPL body vertex when equal to 1, w are the blend weights of skeleton joint prediction function J(.); draping, using a trained deep neural network, via the one or more hardware processors, the constructed 3D skinned garment for the SMPL-created 3D digital human body of the subject based on (i) the garment vertices offsets, and (ii) a plurality of pre-defined ground truth garment intermediate features obtained as an output of a VGG 19 network that is input with front, back, right, left and top rendered images of the 3D skinned garment, wherein the deep neural network is trained based on a plurality of losses for accurate fitness; and visually rendering the resulting draped 3D garment on the 3D digital human body of the subject. receiving, via one or more hardware processors, a plurality of human body inputs which includes data representation of a subject; creating, using a skinned multi person linear, SMPL, body model, via the one or more hardware processors, a 3D digital human body of the subject based on a body shape, β, and a body pose, θ, from the plurality of human body inputs, wherein the SMPL body model outputs the 3D human body as a function of the body pose and body shape and comprises a linear function T b (β,θ) adding body pose and body shape dependent deformations; predicting, using a multi-layer perceptron model, via the one or more hardware processors, garment vertices offsets, Ô, based on the plurality of human body inputs and garment style coefficients, wherein the garment mesh vertices are defined as a subset of the SMPL model mesh vertices; constructing, using a garment skinning function, via the one or more hardware processors, a 3D skinned garment for the subject using the garment vertices offsets, Ô, wherein the garment skinning function is defined by the equation G ^ = G θ, β, Ô = W T g θ β O ^, J β, θ, w; and Where, W is a garment skinning function, J (.) is a skeleton joint prediction function, - T g θ, β, Ô = IT b β θ + Ô, W is a garment skinning function, J (.) is a skeleton joint prediction function, - T g θ, β, Ô = IT b β θ + Ô, wherein I is an indicator matrix, whose element i mn indicates that the m th garment vertex is associated with the n th SMPL body vertex when equal to 1, w are the blend weights of skeleton joint prediction function J(.); draping, using a trained deep neural network, via the one or more hardware processors, the constructed 3D skinned garment for the SMPL-created 3D digital human body of the subject based on (i) the garment vertices offsets, and (ii) a plurality of pre-defined ground truth garment intermediate features obtained as an output of a VGG 19 network that is input with front, back, right, left and top rendered images of the 3D skinned garment, wherein the deep neural network is trained based on a plurality of losses for accurate fitness; and visually w are the blend weights of skeleton joint prediction function J(.); draping, using a trained deep neural network, via the one or more hardware processors, the constructed 3D skinned garment for the SMPL-created 3D digital human body of the subject based on (i) the garment vertices offsets, and (ii) a plurality of pre-defined ground truth garment intermediate features obtained as an output of a VGG 19 network that is input with front, back, right, left and top rendered images of the 3D skinned garment, wherein the deep neural network is trained based on a plurality of losses for accurate fitness; and visually rendering the resulting draped 3D garment on the 3D digital human body of the subject.