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.

该研究通过基于束调整的算法从单眼视频中恢复准确的3D人体姿势和网格，通过在整个序列上重建人体来提供额外的约束并解决歧义问题。同时，该研究提出了一个包含超过3百万帧来自Kinetics的YouTube视频数据集，能够提高3D运动估计的准确性，该算法具有实际意义。

利用时间上下文对野外三维人体姿态估计进行改进