MMVP: A Multimodal MoCap Dataset with Vision and Pressure Sensors
CVPR 2024(2024)
摘要
Foot contact is an important cue not only for human motion capture but also
for motion understanding and physically plausible motion generation. However,
most of the foot-contact annotations in existing datasets are estimated by
purely visual matching and distance thresholding, which results in low accuracy
and coarse granularity. Even though existing multimodal datasets
synergistically capture plantar pressure (foot contact) and visual signals,
they are specifically designed for small-range and slow motion such as Taiji
Quan and Yoga. Therefore, 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://haolyuan.github.io/MMVP-Dataset/.
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