Method for constructing a two-dimensional or three-dimensional image model (M1; M) of an object from at least one image data input (IV1; IV1-IVn) providing a still image or a sequence of images, said method comprising the steps of - extracting at least one state (PS1; PS1-PSn) comprising a configuration of object features which features being represented by a set of values of said at least one image data input (IV1; IV1-IVn), and a state (PSMF) comprising a configuration of object features which features being represented by a set of values of an intermediate learning model (MF; MIF) - determining a target state by either performing a weighted combination of said at least one state (PS1; PS1-PSn) of said at least one image data input, and of the state (PSMF) of said intermediate learning model (MF; MIF), with the weights reflecting confidences of the states, the confidences being determined during the state extraction, or by selecting one of the at least one state of said at least one image data input, and of the state of said intermediate learning model, - performing at least one image transformation by using the determined target state on said at least one image data input (IV1; IV1-IVn), thereby generating at least one transformed image (IV1T; IV1T-IVnT), - aggregating said at least one transformed image (IV1T; IV1T-IVnt) with intermediate learning model (MF; MIF; MIT; MFT) information into a single two-dimensional or 3-dimensional image, thereby generating an updated estimate of said image model (M1; M), - providing said updated estimate of said image model (M1; M) as said image model (M1;M) to an output while also - providing said updated estimate of said image model (M1;M) in a feedback loop to a model object learning module for deriving an update of said intermediate learning model (MF, MIF), - repeating the previous steps in an iterative process - extracting at least one state (PS1; PS1-PSn) comprising a configuration of object features which features being represented by a set of values of said at least one image data input (IV1; IV1-IVn), and a state (PSMF) comprising a configuration of object features which features being represented by a set of values of an intermediate learning model (MF; MIF) - determining a target state by either performing a weighted combination of said at least one state (PS1; PS1-PSn) of said at least one image data input, and of the state (PSMF) of said intermediate learning model (MF; MIF), with the weights reflecting confidences of the states, the confidences being determined during the state extraction, or by selecting one of the at least one state of said at least one image data input, and of the state of said intermediate learning model, - performing at least one image transformation by using the determined target state on said at least one image data input (IV1; IV1-IVn), thereby generating at least one transformed image (IV1T; IV1T-IVnT), - aggregating said at least one transformed image (IV1T; IV1T-IVnt) with intermediate learning model (MF; MIF; MIT; MFT) information into a single two-dimensional or 3-dimensional image, thereby generating an updated estimate of said image model (M1; M), - providing said updated estimate of said image model (M1; M) as said image model (M1;M) to an output while also - providing said updated estimate of said image model (M1;M) in a feedback loop to a model object learning module for deriving an update of said intermediate learning model (MF, MIF), - repeating the previous steps in an iterative process