Rethinking Pose in 3D: Multi-stage Refinement and Recovery for Markerless Motion Capture

4 Aug 2018  ·  Denis Tome, Matteo Toso, Lourdes Agapito, Chris Russell ·

We propose a CNN-based approach for multi-camera markerless motion capture of the human body. Unlike existing methods that first perform pose estimation on individual cameras and generate 3D models as post-processing, our approach makes use of 3D reasoning throughout a multi-stage approach. This novelty allows us to use provisional 3D models of human pose to rethink where the joints should be located in the image and to recover from past mistakes. Our principled refinement of 3D human poses lets us make use of image cues, even from images where we previously misdetected joints, to refine our estimates as part of an end-to-end approach. Finally, we demonstrate how the high-quality output of our multi-camera setup can be used as an additional training source to improve the accuracy of existing single camera models.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
3D Human Pose Estimation Human3.6M Multi-camera 3D human pose network Average MPJPE (mm) 52.8 # 159


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