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.

本研究提出了一种新的方法来进行视频中的 3D 人体姿势估计。在此方法中，作者从人类骨骼解剖学中获得灵感，将任务分解为骨方向预测和骨长度预测两个子任务，以从全局信息中进行高准确性的骨长度预测。作者进一步提出了全卷积传播体系结构和新型关节位移损失，用于预测不同骨头的方向，有效地提高了模型性能。最后，作者引入了隐式关注机制，将 2D 关键点可见性分数作为额外指导加入模型，显著减少了许多具有深度模糊的挑战性姿势。经过全面的评估，证实该模型在 Human3.6M 和 MPI-INF-3DHP 数据集上的效果优于之前的最佳结果。