  We present a bundle-adjustment-based algorithm for recovering accurate 3D
human pose and meshes from monocular videos. Unlike previous algorithms which
operate on single frames, we show that reconstructing a person over an entire
sequence gives extra constraints that can resolve ambiguities. This is because
videos often give multiple views of a person, yet the overall body shape does
not change and 3D positions vary slowly. Our method improves not only on
standard mocap-based datasets like Human 3.6M -- where we show quantitative
improvements -- but also on challenging in-the-wild datasets such as Kinetics.
Building upon our algorithm, we present a new dataset of more than 3 million
frames of YouTube videos from Kinetics with automatically generated 3D poses
and meshes. We show that retraining a single-frame 3D pose estimator on this
data improves accuracy on both real-world and mocap data by evaluating on the
3DPW and HumanEVA datasets.
