1 research outputs found
Multi-View Matching (MVM): Facilitating Multi-Person 3D Pose Estimation Learning with Action-Frozen People Video
To tackle the challeging problem of multi-person 3D pose estimation from a
single image, we propose a multi-view matching (MVM) method in this work. The
MVM method generates reliable 3D human poses from a large-scale video dataset,
called the Mannequin dataset, that contains action-frozen people immitating
mannequins. With a large amount of in-the-wild video data labeled by 3D
supervisions automatically generated by MVM, we are able to train a neural
network that takes a single image as the input for multi-person 3D pose
estimation. The core technology of MVM lies in effective alignment of 2D poses
obtained from multiple views of a static scene that has a strong geometric
constraint. Our objective is to maximize mutual consistency of 2D poses
estimated in multiple frames, where geometric constraints as well as appearance
similarities are taken into account simultaneously. To demonstrate the
effectiveness of 3D supervisions provided by the MVM method, we conduct
experiments on the 3DPW and the MSCOCO datasets and show that our proposed
solution offers the state-of-the-art performance.Comment: 16 pages, 6 figures, submitted JVC