To recover the underlying paths from a given signature representation can not only give confidence in using signature as features in machine learning tasks but also augment the data. The 'Deep signature transforms' allows the signature transformation to act as a layer in a trainable neural network. Inspired by it, we tried to recover different types of underlying paths from a given signature-based representation. By visualizing the results, we discussed the effects of different hyper-parameters for our signature feature set. Also, we tried to explore further applications, eg data augmentation and frame interpolation.