We present the initial design of a motion reconstruction framework for character animation which encompasses the use of
supervised and unsupervised learning techniques for the retrieval and synthesis of new realistic motion. Taking advantage of
the large amounts of Motion Capture data accumulated over the years, our aim is to shorten animation production times by
providing animators with more control over the specification of high-level parameters and a user-friendly way of retrieving and
reusing this data, applying clustering to organize the human motion database and Neural Networks for motion generatio