3D Pose Estimation and Future Motion Prediction from 2D Images
This addresses the need for accurate 3D human motion analysis in applications like robotics and animation, but it is incremental as it builds on existing methods with specific improvements.
The paper tackles the joint problem of estimating 3D human poses and predicting future 3D motions from 2D image sequences, achieving competitive performance on benchmarks like Human3.6M and HumanEva-I.
This paper considers to jointly tackle the highly correlated tasks of estimating 3D human body poses and predicting future 3D motions from RGB image sequences. Based on Lie algebra pose representation, a novel self-projection mechanism is proposed that naturally preserves human motion kinematics. This is further facilitated by a sequence-to-sequence multi-task architecture based on an encoder-decoder topology, which enables us to tap into the common ground shared by both tasks. Finally, a global refinement module is proposed to boost the performance of our framework. The effectiveness of our approach, called PoseMoNet, is demonstrated by ablation tests and empirical evaluations on Human3.6M and HumanEva-I benchmark, where competitive performance is obtained comparing to the state-of-the-arts.