The perception of biological motion is crucial for the survival of human beings. By examining the models of biological motion perception, it is helpful to understand the complex process. The previous models emphasized how visual system encodes biological motion. The kinetic-geometric model for visual vector analysis originally developed in the study of perception of motion combinations of the mechanical type was applied to these biological motion patterns. For the "planarity assumption" in the interpretation of biological motion, the specific problem addressed is how the three dimensional structure and motions of animal limbs may be computed from the two dimensional motions of their projected images. Most recent studies taken the neural mechanism of biological motion perception into account. Hierarchical neural model by Giese and Poggio use a neurophysiologically plausible and quantitative model as a tool for organizing and making sense of the experimental data, despite their growing size and complexity. Template-matching model from configural form cues is addressed by Lange and Lappe. They present a computational model based on neurally plausible assumptions to elucidate the contributions of motion and form signals to biological motion perception and the computations in the underlying brain network. The model simulates receptive fields for images of the static human body, as found by neuroimaging studies, and temporally integrates their responses by leaky integrator neurons. The model reveals a high correlation to data obtained by neurophysiological, neuroimaging, and psychophysical studies. These above models were proposed to explain key experimental results and plan new experiments relating to the recognition of biological movements. The paper also points to issues that cannot be answered by these models and by the available experimental results.