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In this paper we present a novel method to reconstruct the 3D posture of flying bats from sparse multiple view video. Specifically, we incorporate biomechanical and geometric knowledge about bats into an articulated model. We then estimate the bats time-varying pose by tracking a set of known markers using a Square Root Unscented Kalman filtering method augmented with video optical flow information. Our method scales easily to multiple views, elegantly handles missing and occluded markers, and has a versatility in the type and complexity of the tracking model. To demonstrate the performance of the reconstruction method, we apply our technique to estimate the parameters of a 52 degree of freedom articulated model of a bat from a real-world flight sequence. We further assess our algorithms performance by quantifying its ability to recover model parameters accurately for a realistic simulated flight sequence.