  There exist challenging problems in 3D human pose estimation mission, such as
poor performance caused by occlusion and self-occlusion. Recently, IMU-vision
sensor fusion is regarded as valuable for solving these problems. However,
previous researches on the fusion of IMU and vision data, which is
heterogeneous, fail to adequately utilize either IMU raw data or reliable
high-level vision features. To facilitate a more efficient sensor fusion, in
this work we propose a framework called \emph{FusePose} under a parametric
human kinematic model. Specifically, we aggregate different information of IMU
or vision data and introduce three distinctive sensor fusion approaches:
NaiveFuse, KineFuse and AdaDeepFuse. NaiveFuse servers as a basic approach that
only fuses simplified IMU data and estimated 3D pose in euclidean space. While
in kinematic space, KineFuse is able to integrate the calibrated and aligned
IMU raw data with converted 3D pose parameters. AdaDeepFuse further develops
this kinematical fusion process to an adaptive and end-to-end trainable manner.
Comprehensive experiments with ablation studies demonstrate the rationality and
superiority of the proposed framework. The performance of 3D human pose
estimation is improved compared to the baseline result. On Total Capture
dataset, KineFuse surpasses previous state-of-the-art which uses IMU only for
testing by 8.6\%. AdaDeepFuse surpasses state-of-the-art which uses IMU for
both training and testing by 8.5\%. Moreover, we validate the generalization
capability of our framework through experiments on Human3.6M dataset.
