Although most naturally occurring objects move in a nonrigid manner, the vast majority of the motion research in computer vision has focused on rigid objects due to mathematical elegance and legacy. Driven recently by new applications in telecommunications, medicine, and industry, a new body of research is beginning to emerge that is focused on the recovery of structure and motion for nonrigid objects. Most of these methods, however, have abandoned the large body of work in feature-based rigid body motion recovery in favor of modelbased techniques, which unfortunately limits their abilities in completely unknown environments. Four token-based, rigid body structure from motion (SFM) algorithms are modified for the recovery of structure from nonrigid motion by replacing the rigidity constraint with the temporal fusion of an image sequence. The results of numerous experiments using simulated imagery are also presented to demonstrate and characterize the performance of the four modified techniques.