  Several video-based 3D pose and shape estimation algorithms have been
proposed to resolve the temporal inconsistency of single-image-based methods.
However it still remains challenging to have stable and accurate
reconstruction. In this paper, we propose a new framework Deep Two-Stream Video
Inference for Human Body Pose and Shape Estimation (DTS-VIBE), to generate 3D
human pose and mesh from RGB videos. We reformulate the task as a
multi-modality problem that fuses RGB and optical flow for more reliable
estimation. In order to fully utilize both sensory modalities (RGB or optical
flow), we train a two-stream temporal network based on transformer to predict
SMPL parameters. The supplementary modality, optical flow, helps to maintain
temporal consistency by leveraging motion knowledge between two consecutive
frames. The proposed algorithm is extensively evaluated on the Human3.6 and
3DPW datasets. The experimental results show that it outperforms other
state-of-the-art methods by a significant margin.
