VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment

• URL: http://arxiv.org/abs/2004.06239v4
• Date: Mon, 24 Aug 2020 11:01:32 GMT
• Title: VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment
• Authors: Hanyue Tu, Chunyu Wang, Wenjun Zeng
• Abstract summary: We present an approach to estimate 3D poses of multiple people from multiple camera views. We present an end-to-end solution which operates in the $3$D space, therefore avoids making incorrect decisions in the 2D space. We propose Pose Regression Network (PRN) to estimate a detailed 3D pose for each proposal.
• Score: 80.77351380961264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present an approach to estimate 3D poses of multiple people from multiple camera views. In contrast to the previous efforts which require to establish cross-view correspondence based on noisy and incomplete 2D pose estimations, we present an end-to-end solution which directly operates in the $3$D space, therefore avoids making incorrect decisions in the 2D space. To achieve this goal, the features in all camera views are warped and aggregated in a common 3D space, and fed into Cuboid Proposal Network (CPN) to coarsely localize all people. Then we propose Pose Regression Network (PRN) to estimate a detailed 3D pose for each proposal. The approach is robust to occlusion which occurs frequently in practice. Without bells and whistles, it outperforms the state-of-the-arts on the public datasets. Code will be released at https://github.com/microsoft/multiperson-pose-estimation-pytorch.

Related papers

• MPL: Lifting 3D Human Pose from Multi-view 2D Poses [75.26416079541723]
  We propose combining 2D pose estimation, for which large and rich training datasets exist, and 2D-to-3D pose lifting, using a transformer-based network. Our experiments demonstrate decreases up to 45% in MPJPE errors compared to the 3D pose obtained by triangulating the 2D poses.
  arXiv  Detail & Related papers  (2024-08-20T12:55:14Z)
• Multi-View Person Matching and 3D Pose Estimation with Arbitrary Uncalibrated Camera Networks [36.49915280876899]
  Cross-view person matching and 3D human pose estimation in multi-camera networks are difficult when the cameras are extrinsically uncalibrated. Existing efforts require large amounts of 3D data for training neural networks or known camera poses for geometric constraints to solve the problem. We present a method, PME, that solves the two tasks without requiring either information.
  arXiv  Detail & Related papers  (2023-12-04T01:28:38Z)
• 3D Human Pose Estimation in Multi-View Operating Room Videos Using Differentiable Camera Projections [2.486571221735935]
  We propose to directly optimise for localisation in 3D by training 2D CNNs end-to-end based on a 3D loss. Using videos from the MVOR dataset, we show that this end-to-end approach outperforms optimisation in 2D space.
  arXiv  Detail & Related papers  (2022-10-21T09:00:02Z)
• Shape-aware Multi-Person Pose Estimation from Multi-View Images [47.13919147134315]
  Our proposed coarse-to-fine pipeline first aggregates noisy 2D observations from multiple camera views into 3D space. The final pose estimates are attained from a novel optimization scheme which links high-confidence multi-view 2D observations and 3D joint candidates.
  arXiv  Detail & Related papers  (2021-10-05T20:04:21Z)
• VoxelTrack: Multi-Person 3D Human Pose Estimation and Tracking in the Wild [98.69191256693703]
  We present VoxelTrack for multi-person 3D pose estimation and tracking from a few cameras which are separated by wide baselines. It employs a multi-branch network to jointly estimate 3D poses and re-identification (Re-ID) features for all people in the environment. It outperforms the state-of-the-art methods by a large margin on three public datasets including Shelf, Campus and CMU Panoptic.
  arXiv  Detail & Related papers  (2021-08-05T08:35:44Z)
• Multi-View Multi-Person 3D Pose Estimation with Plane Sweep Stereo [71.59494156155309]
  Existing approaches for multi-view 3D pose estimation explicitly establish cross-view correspondences to group 2D pose detections from multiple camera views. We present our multi-view 3D pose estimation approach based on plane sweep stereo to jointly address the cross-view fusion and 3D pose reconstruction in a single shot.
  arXiv  Detail & Related papers  (2021-04-06T03:49:35Z)
• Fusing Wearable IMUs with Multi-View Images for Human Pose Estimation: A Geometric Approach [76.10879433430466]
  We propose to estimate 3D human pose from multi-view images and a few IMUs attached at person's limbs. It operates by firstly detecting 2D poses from the two signals, and then lifting them to the 3D space. The simple two-step approach reduces the error of the state-of-the-art by a large margin on a public dataset.
  arXiv  Detail & Related papers  (2020-03-25T00:26:54Z)
• Cross-View Tracking for Multi-Human 3D Pose Estimation at over 100 FPS [13.191601826570786]
  We present a novel solution for multi-human 3D pose estimation from multiple calibrated camera views. It takes 2D poses in different camera coordinates as inputs and aims for the accurate 3D poses in the global coordinate. We propose a new large-scale multi-human dataset with 12 to 28 camera views.
  arXiv  Detail & Related papers  (2020-03-09T08:54:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.