Facial Expression Space Learning

Animation of facial speech and expressions has experienced increased attention recently. Most current research focuses on techniques for capturing, synthesizing, and retargeting facial expressions. Little attention has been paid to the problem of controlling and modifying the expression itself. We present techniques that separate video data into expressive features and underlying content. This allows, for example, a sequence originally recorded with a happy expression to be modified so that the speaker appears to be speaking with an angry or neutral expression. Although the expression has been modified, the new sequences maintain the same visual speech content as the original sequence. The facial expression space that allows these transformations is learned with the aid of a factorization model.