  In this work, we propose a new solution to 3D human pose estimation in
videos. Instead of directly regressing the 3D joint locations, we draw
inspiration from the human skeleton anatomy and decompose the task into bone
direction prediction and bone length prediction, from which the 3D joint
locations can be completely derived. Our motivation is the fact that the bone
lengths of a human skeleton remain consistent across time. This promotes us to
develop effective techniques to utilize global information across all the
frames in a video for high-accuracy bone length prediction. Moreover, for the
bone direction prediction network, we propose a fully-convolutional propagating
architecture with long skip connections. Essentially, it predicts the
directions of different bones hierarchically without using any time-consuming
memory units e.g. LSTM). A novel joint shift loss is further introduced to
bridge the training of the bone length and bone direction prediction networks.
Finally, we employ an implicit attention mechanism to feed the 2D keypoint
visibility scores into the model as extra guidance, which significantly
mitigates the depth ambiguity in many challenging poses. Our full model
outperforms the previous best results on Human3.6M and MPI-INF-3DHP datasets,
where comprehensive evaluation validates the effectiveness of our model.
