  3D Human body pose and shape estimation within a temporal sequence can be
quite critical for understanding human behavior. Despite the significant
progress in human pose estimation in the recent years, which are often based on
single images or videos, human motion estimation on live stream videos is still
a rarely-touched area considering its special requirements for real-time output
and temporal consistency. To address this problem, we present a temporally
embedded 3D human body pose and shape estimation (TePose) method to improve the
accuracy and temporal consistency of pose estimation in live stream videos.
TePose uses previous predictions as a bridge to feedback the error for better
estimation in the current frame and to learn the correspondence between data
frames and predictions in the history. A multi-scale spatio-temporal graph
convolutional network is presented as the motion discriminator for adversarial
training using datasets without any 3D labeling. We propose a sequential data
loading strategy to meet the special start-to-end data processing requirement
of live stream. We demonstrate the importance of each proposed module with
extensive experiments. The results show the effectiveness of TePose on
widely-used human pose benchmarks with state-of-the-art performance.
