Bayesian Reconstruction of 3D Human Motion from Single-Camera Video

Part of Advances in Neural Information Processing Systems 12 (NIPS 1999)

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Nicholas Howe, Michael Leventon, William Freeman


The three-dimensional motion of humans is underdetermined when the observation is limited to a single camera, due to the inherent 3D ambi(cid:173) guity of 2D video. We present a system that reconstructs the 3D motion of human subjects from single-camera video, relying on prior knowledge about human motion, learned from training data, to resolve those am(cid:173) biguities. After initialization in 2D, the tracking and 3D reconstruction is automatic; we show results for several video sequences. The results show the power of treating 3D body tracking as an inference problem.