  Foot contact is an important cue for human motion capture, understanding, and
generation. Existing datasets tend to annotate dense foot contact using visual
matching with thresholding or incorporating pressure signals. However, these
approaches either suffer from low accuracy or are only designed for small-range
and slow motion. There is still a lack of a vision-pressure multimodal dataset
with large-range and fast human motion, as well as accurate and dense
foot-contact annotation. To fill this gap, we propose a Multimodal MoCap
Dataset with Vision and Pressure sensors, named MMVP. MMVP provides accurate
and dense plantar pressure signals synchronized with RGBD observations, which
is especially useful for both plausible shape estimation, robust pose fitting
without foot drifting, and accurate global translation tracking. To validate
the dataset, we propose an RGBD-P SMPL fitting method and also a
monocular-video-based baseline framework, VP-MoCap, for human motion capture.
Experiments demonstrate that our RGBD-P SMPL Fitting results significantly
outperform pure visual motion capture. Moreover, VP-MoCap outperforms SOTA
methods in foot-contact and global translation estimation accuracy. We believe
the configuration of the dataset and the baseline frameworks will stimulate the
research in this direction and also provide a good reference for MoCap
applications in various domains. Project page:
https://metaverse-ai-lab-thu.github.io/MMVP-Dataset/.
