SMAP: Single-Shot Multi-Person Absolute 3D Pose Estimation

• URL: http://arxiv.org/abs/2008.11469v1
• Date: Wed, 26 Aug 2020 09:56:07 GMT
• Title: SMAP: Single-Shot Multi-Person Absolute 3D Pose Estimation
• Authors: Jianan Zhen, Qi Fang, Jiaming Sun, Wentao Liu, Wei Jiang, Hujun Bao, Xiaowei Zhou
• Abstract summary: We propose a novel system that first regresses a set of 2.5D representations of body parts and then reconstructs the 3D absolute poses based on these 2.5D representations with a depth-aware part association algorithm. Such a single-shot bottom-up scheme allows the system to better learn and reason about the inter-person depth relationship, improving both 3D and 2D pose estimation.
• Score: 46.85865451812981
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recovering multi-person 3D poses with absolute scales from a single RGB image is a challenging problem due to the inherent depth and scale ambiguity from a single view. Addressing this ambiguity requires to aggregate various cues over the entire image, such as body sizes, scene layouts, and inter-person relationships. However, most previous methods adopt a top-down scheme that first performs 2D pose detection and then regresses the 3D pose and scale for each detected person individually, ignoring global contextual cues. In this paper, we propose a novel system that first regresses a set of 2.5D representations of body parts and then reconstructs the 3D absolute poses based on these 2.5D representations with a depth-aware part association algorithm. Such a single-shot bottom-up scheme allows the system to better learn and reason about the inter-person depth relationship, improving both 3D and 2D pose estimation. The experiments demonstrate that the proposed approach achieves the state-of-the-art performance on the CMU Panoptic and MuPoTS-3D datasets and is applicable to in-the-wild videos.

Related papers

• Unsupervised Multi-Person 3D Human Pose Estimation From 2D Poses Alone [4.648549457266638]
  We present one of the first studies investigating the feasibility of unsupervised multi-person 2D-3D pose estimation. Our method involves independently lifting each subject's 2D pose to 3D, before combining them in a shared 3D coordinate system. This by itself enables us to retrieve an accurate 3D reconstruction of their poses.
  arXiv  Detail & Related papers  (2023-09-26T11:42:56Z)
• MPM: A Unified 2D-3D Human Pose Representation via Masked Pose Modeling [59.74064212110042]
  mpmcan handle multiple tasks including 3D human pose estimation, 3D pose estimation from cluded 2D pose, and 3D pose completion in a textocbfsingle framework. We conduct extensive experiments and ablation studies on several widely used human pose datasets and achieve state-of-the-art performance on MPI-INF-3DHP.
  arXiv  Detail & Related papers  (2023-06-29T10:30:00Z)
• Permutation-Invariant Relational Network for Multi-person 3D Pose Estimation [46.38290735670527]
  Recovering multi-person 3D poses from a single RGB image is a severely ill-conditioned problem. Recent works have shown promising results by simultaneously reasoning for different people but in all cases within a local neighborhood. PI-Net introduces a self-attention block to reason for all people in the image at the same time and refine potentially noisy initial 3D poses. In this paper, we model people interactions at a whole, independently of their number, and in a permutation-invariant manner building upon the Set Transformer.
  arXiv  Detail & Related papers  (2022-04-11T07:23:54Z)
• 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)
• PandaNet : Anchor-Based Single-Shot Multi-Person 3D Pose Estimation [35.791868530073955]
  We present PandaNet, a new single-shot, anchor-based and multi-person 3D pose estimation approach. The proposed model performs bounding box detection and, for each detected person, 2D and 3D pose regression into a single forward pass. It does not need any post-processing to regroup joints since the network predicts a full 3D pose for each bounding box.
  arXiv  Detail & Related papers  (2021-01-07T10:32:17Z)
• Synthetic Training for Monocular Human Mesh Recovery [100.38109761268639]
  This paper aims to estimate 3D mesh of multiple body parts with large-scale differences from a single RGB image. The main challenge is lacking training data that have complete 3D annotations of all body parts in 2D images. We propose a depth-to-scale (D2S) projection to incorporate the depth difference into the projection function to derive per-joint scale variants.
  arXiv  Detail & Related papers  (2020-10-27T03:31:35Z)
• 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)

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.