Learning to Estimate 3D Human Pose from Point Cloud

• URL: http://arxiv.org/abs/2212.12910v1
• Date: Sun, 25 Dec 2022 14:22:01 GMT
• Title: Learning to Estimate 3D Human Pose from Point Cloud
• Authors: Yufan Zhou, Haiwei Dong, and Abdulmotaleb El Saddik
• Abstract summary: We propose a deep human pose network for 3D pose estimation by taking the point cloud data as input data to model the surface of complex human structures. Our experiments on two public datasets show that our approach achieves higher accuracy than previous state-of-art methods.
• Score: 13.27496851711973
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D pose estimation is a challenging problem in computer vision. Most of the existing neural-network-based approaches address color or depth images through convolution networks (CNNs). In this paper, we study the task of 3D human pose estimation from depth images. Different from the existing CNN-based human pose estimation method, we propose a deep human pose network for 3D pose estimation by taking the point cloud data as input data to model the surface of complex human structures. We first cast the 3D human pose estimation from 2D depth images to 3D point clouds and directly predict the 3D joint position. Our experiments on two public datasets show that our approach achieves higher accuracy than previous state-of-art methods. The reported results on both ITOP and EVAL datasets demonstrate the effectiveness of our method on the targeted tasks.

Related papers

• Decanus to Legatus: Synthetic training for 2D-3D human pose lifting [26.108023246654646]
  We propose an algorithm to generate infinite 3D synthetic human poses (Legatus) from a 3D pose distribution based on 10 initial handcrafted 3D poses (Decanus) Our results show that we can achieve 3D pose estimation performance comparable to methods using real data from specialized datasets but in a zero-shot setup, showing the potential of our framework.
  arXiv  Detail & Related papers  (2022-10-05T13:10:19Z)
• SPGNet: Spatial Projection Guided 3D Human Pose Estimation in Low Dimensional Space [14.81199315166042]
  We propose a method for 3D human pose estimation that mixes multi-dimensional re-projection into supervised learning. Based on the estimation results for the dataset Human3.6M, our approach outperforms many state-of-the-art methods both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2022-06-04T00:51:00Z)
• PedRecNet: Multi-task deep neural network for full 3D human pose and orientation estimation [0.0]
  multitask network supports various deep neural network based pedestrian detection functions. Network architecture is relatively simple, yet powerful, and easily adaptable for further research and applications.
  arXiv  Detail & Related papers  (2022-04-25T10:47:01Z)
• PONet: Robust 3D Human Pose Estimation via Learning Orientations Only [116.1502793612437]
  We propose a novel Pose Orientation Net (PONet) that is able to robustly estimate 3D pose by learning orientations only. PONet estimates the 3D orientation of these limbs by taking advantage of the local image evidence to recover the 3D pose. We evaluate our method on multiple datasets, including Human3.6M, MPII, MPI-INF-3DHP, and 3DPW.
  arXiv  Detail & Related papers  (2021-12-21T12:48:48Z)
• Learning Temporal 3D Human Pose Estimation with Pseudo-Labels [3.0954251281114513]
  We present a simple, yet effective, approach for self-supervised 3D human pose estimation. We rely on triangulating 2D body pose estimates of a multiple-view camera system. Our method achieves state-of-the-art performance in the Human3.6M and MPI-INF-3DHP benchmarks.
  arXiv  Detail & Related papers  (2021-10-14T17:40:45Z)
• Residual Pose: A Decoupled Approach for Depth-based 3D Human Pose Estimation [18.103595280706593]
  We leverage recent advances in reliable 2D pose estimation with CNN to estimate the 3D pose of people from depth images. Our approach achieves very competitive results both in accuracy and speed on two public datasets.
  arXiv  Detail & Related papers  (2020-11-10T10:08:13Z)
• Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose [70.23652933572647]
  We propose a novel graph convolutional neural network (GraphCNN)-based system that estimates the 3D coordinates of human mesh vertices directly from the 2D human pose. We show that our Pose2Mesh outperforms the previous 3D human pose and mesh estimation methods on various benchmark datasets.
  arXiv  Detail & Related papers  (2020-08-20T16:01:56Z)
• Cascaded deep monocular 3D human pose estimation with evolutionary training data [76.3478675752847]
  Deep representation learning has achieved remarkable accuracy for monocular 3D human pose estimation. This paper proposes a novel data augmentation method that is scalable for massive amount of training data. Our method synthesizes unseen 3D human skeletons based on a hierarchical human representation and synthesizings inspired by prior knowledge.
  arXiv  Detail & Related papers  (2020-06-14T03:09:52Z)
• Towards Generalization of 3D Human Pose Estimation In The Wild [73.19542580408971]
  3DBodyTex.Pose is a dataset that addresses the task of 3D human pose estimation in-the-wild. 3DBodyTex.Pose offers high quality and rich data containing 405 different real subjects in various clothing and poses, and 81k image samples with ground-truth 2D and 3D pose annotations.
  arXiv  Detail & Related papers  (2020-04-21T13:31:58Z)
• Self-Supervised 3D Human Pose Estimation via Part Guided Novel Image Synthesis [72.34794624243281]
  We propose a self-supervised learning framework to disentangle variations from unlabeled video frames. Our differentiable formalization, bridging the representation gap between the 3D pose and spatial part maps, allows us to operate on videos with diverse camera movements.
  arXiv  Detail & Related papers  (2020-04-09T07:55:01Z)
• HandVoxNet: Deep Voxel-Based Network for 3D Hand Shape and Pose Estimation from a Single Depth Map [72.93634777578336]
  We propose a novel architecture with 3D convolutions trained in a weakly-supervised manner. The proposed approach improves over the state of the art by 47.8% on the SynHand5M dataset. Our method produces visually more reasonable and realistic hand shapes on NYU and BigHand2.2M datasets.
  arXiv  Detail & Related papers  (2020-04-03T14:27:16Z)

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.