This paper presents a data-driven procedural model for the kinematic animation of human walking. The use of data yields realistic looking gait, while the procedural model yields flexibility. We present a new motion data representation, the sagittal elevation angles, and present biomechanical evidence that these angles have a stereotyped pattern across many different walking situations, implying their reusability as a motion data source. We also sketch our algorithm for animating human gait based on sagittal elevation angle data, which allows us to generate curved locomotion on uneven terrain with stylistic variation without requiring new datasets.