SIGEDA has a large potential for use by AI developers, as it solves the issue of needing large amounts of pictures and/or videos to train convolutional neural networks (CNN). Furthermore, these synthetic images/videos provide precise 3D locations of the subject in the image, not estimated as is usually done from a photo. Also very important: the time-consuming labelling is done automatically.
The variety of datasets can be balanced as needed, avoiding bias or ethics issues, and allow for creation of images difficult to obtain, e.g. evolution over a large span of time (crop growth, symptoms of diseases).
SIGEDA used with images allows for precise detection of objects or people, while use with videos enables the detection of movements, as the dimension of time is added. An example of a past application is controlling patients in the execution of rehabilitation exercises.
