A Multi-scale Recalibrated Approach for 3D Human Pose Estimation

The major challenge for 3D human pose estimation is the ambiguity in the process of regressing 3D poses from 2D. The ambiguity is introduced by the poor exploiting of the image cues especially the spatial relations. Previous works try to use a weakly-supervised method to constrain illegal spatial relations instead of leverage image cues directly. We follow the weakly-supervised method to train an end-to-end network by first detecting 2D body joints heatmaps, and then constraining 3D regression through 2D heatmaps. To further utilize the inherent spatial relations, we propose to use a multi-scale recalibrated approach to regress 3D pose. The recalibrated approach is integrated into the network as an independent module, and the scale factor is altered to capture information in different resolutions. With the additional multi-scale recalibration modules, the spatial information in pose is better exploited in the regression process. The whole network is fine-tuned for the extra parameters. The quantitative result on Human3.6m dataset demonstrates the performance surpasses the state-of-the-art. Qualitative evaluation results on the Human3.6m and in-the-wild MPII datasets show the effectiveness and robustness of our approach which can handle some complex situations such as self-occlusions.