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Estimating human motion from video is an active research area due to its manypotential applications. Most state-of-the-art methods predict human shape andposture estimates for individual images and do not leverage the temporalinformation available in video. Many "in the wild" sequences of human motionare captured by a moving camera, which adds the complication of conflatedcamera and human motion to the estimation. We therefore present BodySLAM, amonocular SLAM system that jointly estimates the position, shape, and postureof human bodies, as well as the camera trajectory. We also introduce a novelhuman motion model to constrain sequential body postures and observe the scaleof the scene. Through a series of experiments on video sequences of humanmotion captured by a moving monocular camera, we demonstrate that BodySLAMimproves estimates of all human body parameters and camera poses when comparedto estimating these separately.

BodySLAM: Joint Camera Localisation, Mapping, and Human Motion Tracking