  Estimating human motion from video is an active research area due to its many
potential applications. Most state-of-the-art methods predict human shape and
posture estimates for individual images and do not leverage the temporal
information available in video. Many "in the wild" sequences of human motion
are captured by a moving camera, which adds the complication of conflated
camera and human motion to the estimation. We therefore present BodySLAM, a
monocular SLAM system that jointly estimates the position, shape, and posture
of human bodies, as well as the camera trajectory. We also introduce a novel
human motion model to constrain sequential body postures and observe the scale
of the scene. Through a series of experiments on video sequences of human
motion captured by a moving monocular camera, we demonstrate that BodySLAM
improves estimates of all human body parameters and camera poses when compared
to estimating these separately.
