  Video 3D human pose estimation aims to localize the 3D coordinates of human
joints from videos. Recent transformer-based approaches focus on capturing the
spatiotemporal information from sequential 2D poses, which cannot model the
contextual depth feature effectively since the visual depth features are lost
in the step of 2D pose estimation. In this paper, we simplify the paradigm into
an end-to-end framework, Instance-guided Video Transformer (IVT), which enables
learning spatiotemporal contextual depth information from visual features
effectively and predicts 3D poses directly from video frames. In particular, we
firstly formulate video frames as a series of instance-guided tokens and each
token is in charge of predicting the 3D pose of a human instance. These tokens
contain body structure information since they are extracted by the guidance of
joint offsets from the human center to the corresponding body joints. Then,
these tokens are sent into IVT for learning spatiotemporal contextual depth. In
addition, we propose a cross-scale instance-guided attention mechanism to
handle the variational scales among multiple persons. Finally, the 3D poses of
each person are decoded from instance-guided tokens by coordinate regression.
Experiments on three widely-used 3D pose estimation benchmarks show that the
proposed IVT achieves state-of-the-art performances.
