Method for transforming an "original" view of an "original" digital three-dimensional model of a dental arch into a hyper-realistic view, said method comprising the following steps: 21) creating a "transformation" learning base that is unpaired and comprises -an input set consisting of "input views" each representing a view of a transformation digital three-dimensional model modelling a dental scene, preferably more than 1000, more than 5000, preferably more than 10 000, preferably more than 30 000, preferably more than 50 000 and preferably more than 100 000 input views, and - an "output" set consisting of "output photos" each representing a dental scene, preferably more than 1000, more than 5000, preferably more than 10 000, preferably more than 30 000, preferably more than 50 000 and preferably more than 100 000 output photos, the output photos being able to be independent of the output views, that is to say not represent the same dental scenes, or alternatively, creating a "transformation" learning base that is paired and consists of more than 1000 "transformation" records, each transformation record comprising: - a "transformation" photo representing a dental scene, and - a view of a "transformation" digital three-dimensional model modelling said dental scene, or "transformation view", the transformation view representing said scene in the same way as the transformation photo; 22) training at least one "transformation" neural network, by way of the transformation learning base, such that it learns how to transform any view of any digital three-dimensional model into a hyper-realistic view; 23) submitting the original view to said at least one transformation neural network, such that it transforms it into a hyper-realistic view. 21) creating a "transformation" learning base that is unpaired and comprises -an input set consisting of "input views" each representing a view of a transformation digital three-dimensional model modelling a dental scene, preferably more than 1000, more than 5000, preferably more than 10 000, preferably more than 30 000, preferably more than 50 000 and preferably more than 100 000 input views, and - an "output" set consisting of "output photos" each representing a dental scene, preferably more than 1000, more than 5000, preferably more than 10 000, preferably more than 30 000, preferably more than 50 000 and preferably more than 100 000 output photos, the output photos being able to be independent of the output views, that is to say not represent the same dental scenes, or alternatively, creating a "transformation" learning base that is paired and consists of more than 1000 "transformation" records, each transformation record comprising: - a "transformation" photo representing a dental scene, and - a view of a "transformation" digital three-dimensional model modelling said dental scene, or "transformation view", the transformation view representing said scene in the same way as the transformation photo; - an "output" set consisting of "output photos" each representing a dental scene, preferably more than 1000, more than 5000, preferably more than 10 000, preferably more than 30 000, preferably more than 50 000 and preferably more than 100 000 output photos, the output photos being able to be independent of the output views, that is to say not represent the same dental scenes, or alternatively, creating a "transformation" learning base that is paired and consists of more than 1000 "transformation" records, each transformation record comprising: - a "transformation" photo representing a dental scene, and - a view of a "transformation" digital three-dimensional model modelling said dental scene, or "transformation view", the transformation view representing said scene in the same way as the transformation photo; - a "transformation" photo representing a dental scene, and - a view of a "transformation" digital three-dimensional model modelling said dental scene, or "transformation view", the transformation view representing said scene in the same way as the transformation photo; 22) training at least one "transformation" neural network, by way of the transformation learning base, such that it learns how to transform any view of any digital three-dimensional model into a hyper-realistic view; 23) submitting the original view to said at least one transformation neural network, such that it transforms it into a hyper-realistic view.