PoseGU: 3D Human Pose Estimation with Novel Human Pose Generator and Unbiased Learning

• URL: http://arxiv.org/abs/2207.03618v1
• Date: Thu, 7 Jul 2022 23:43:53 GMT
• Title: PoseGU: 3D Human Pose Estimation with Novel Human Pose Generator and Unbiased Learning
• Authors: Shannan Guan, Haiyan Lu, Linchao Zhu, Gengfa Fang
• Abstract summary: 3D pose estimation has recently gained substantial interests in computer vision domain. Existing 3D pose estimation methods have a strong reliance on large size well-annotated 3D pose datasets. We propose PoseGU, a novel human pose generator that generates diverse poses with access only to a small size of seed samples.
• Score: 36.609189237732394
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D pose estimation has recently gained substantial interests in computer vision domain. Existing 3D pose estimation methods have a strong reliance on large size well-annotated 3D pose datasets, and they suffer poor model generalization on unseen poses due to limited diversity of 3D poses in training sets. In this work, we propose PoseGU, a novel human pose generator that generates diverse poses with access only to a small size of seed samples, while equipping the Counterfactual Risk Minimization to pursue an unbiased evaluation objective. Extensive experiments demonstrate PoseGU outforms almost all the state-of-the-art 3D human pose methods under consideration over three popular benchmark datasets. Empirical analysis also proves PoseGU generates 3D poses with improved data diversity and better generalization ability.

Related papers

• ManiPose: Manifold-Constrained Multi-Hypothesis 3D Human Pose Estimation [54.86887812687023]
  Most 3D-HPE methods rely on regression models, which assume a one-to-one mapping between inputs and outputs. We propose ManiPose, a novel manifold-constrained multi-hypothesis model capable of proposing multiple candidate 3D poses for each 2D input. Unlike previous multi-hypothesis approaches, our solution is completely supervised and does not rely on complex generative models.
  arXiv  Detail & Related papers  (2023-12-11T13:50:10Z)
• 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)
• Heuristic Weakly Supervised 3D Human Pose Estimation [13.82540778667711]
  weakly supervised 3D human pose (HW-HuP) solution to estimate 3D poses in when no ground truth 3D pose data is available. We show that HW-HuP meaningfully improves upon state-of-the-art models in two practical settings where 3D pose data can hardly be obtained: human poses in bed, and infant poses in the wild.
  arXiv  Detail & Related papers  (2021-05-23T18:40:29Z)
• Adapted Human Pose: Monocular 3D Human Pose Estimation with Zero Real 3D Pose Data [14.719976311208502]
  Training vs. test data domain gaps often negatively affect model performance. We present our adapted human pose (AHuP) approach that addresses adaptation problems in both appearance and pose spaces. AHuP is built around a practical assumption that in real applications, data from target domain could be inaccessible or only limited information can be acquired.
  arXiv  Detail & Related papers  (2021-05-23T01:20:40Z)
• PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation [83.50127973254538]
  Existing 3D human pose estimators suffer poor generalization performance to new datasets. We present PoseAug, a new auto-augmentation framework that learns to augment the available training poses towards a greater diversity.
  arXiv  Detail & Related papers  (2021-05-06T06:57:42Z)
• 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)
• 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)
• Weakly-Supervised 3D Human Pose Learning via Multi-view Images in the Wild [101.70320427145388]
  We propose a weakly-supervised approach that does not require 3D annotations and learns to estimate 3D poses from unlabeled multi-view data. We evaluate our proposed approach on two large scale datasets.
  arXiv  Detail & Related papers  (2020-03-17T08:47:16Z)

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.