Recovering temporally consistent 3D human body pose, shape and motion from a
monocular video is a challenging task due to (self-)occlusions, poor lighting
conditions, complex articulated body poses, depth ambiguity, and limited
availability of annotated data. Further, doing a simple perframe estimation is
insufficient as it leads to jittery and implausible results. In this paper, we
propose a novel method for temporally consistent motion estimation from a
monocular video. Instead of using generic ResNet-like features, our method uses
a body-aware feature representation and an independent per-frame pose and
camera initialization over a temporal window followed by a novel
spatio-temporal feature aggregation by using a combination of self-similarity
and self-attention over the body-aware features and the perframe
initialization. Together, they yield enhanced spatiotemporal context for every
frame by considering remaining past and future frames. These features are used
to predict the pose and shape parameters of the human body model, which are
further refined using an LSTM. Experimental results on the publicly available
benchmark data show that our method attains significantly lower acceleration
error and outperforms the existing state-of-the-art methods over all key
quantitative evaluation metrics, including complex scenarios like partial
occlusion, complex poses and even relatively low illumination.

提出了一种从单眼视频中恢复时间一致的 3D 人体姿势、形状和动作的方法，通过使用具有身体感知特征表示和独立的逐帧姿势和相机初始化，结合自相似性和自注意力等技术进行时空特征聚合，该方法提供了增强的时空上下文，可在各种复杂场景下得到更准确的姿势估计和形状重建。