  We consider the task of estimating 3D human pose and shape from videos. While
existing frame-based approaches have made significant progress, these methods
are independently applied to each image, thereby often leading to inconsistent
predictions. In this work, we present a video-based learning algorithm for 3D
human pose and shape estimation. The key insights of our method are two-fold.
First, to address the inconsistent temporal prediction issue, we exploit
temporal information in videos and propose a self-attention module that jointly
considers short-range and long-range dependencies across frames, resulting in
temporally coherent estimations. Second, we model human motion with a
forecasting module that allows the transition between adjacent frames to be
smooth. We evaluate our method on the 3DPW, MPI-INF-3DHP, and Human3.6M
datasets. Extensive experimental results show that our algorithm performs
favorably against the state-of-the-art methods.
