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AMPose: Alternately Mixed Global-Local Attention Model for 3D Human Pose Estimation

arXiv 2210.04216 ↗First posted 9 Oct 2022cs.CV cs.MMcited by 0

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Abstract (arXiv)

The graph convolutional networks (GCNs) have been applied to model the physically connected and non-local relations among human joints for 3D human pose estimation (HPE). In addition, the purely Transformer-based models recently show promising results in video-based 3D HPE. However, the single-frame method still needs to model the physically connected relations among joints because the feature representations transformed only by global relations via the Transformer neglect information on the human skeleton. To deal with this problem, we propose a novel method in which the Transformer encoder and GCN blocks are alternately stacked, namely AMPose, to combine the global and physically connected relations among joints towards HPE. In the AMPose, the Transformer encoder is applied to connect each joint with all the other joints, while GCNs are applied to capture information on physically connected relations. The effectiveness of our proposed method is evaluated on the Human3.6M dataset. Our model also shows better generalization ability by testing on the MPI-INF-3DHP dataset. Code can be retrieved at https://github.com/erikervalid/AMPose.

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References · 16

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2022A ConvNet for the 2020scited by 3,579
  3. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,089
  4. 2016Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervisioncited by 494
  5. 20183D human pose estimation in video with temporal convolutions and semi-supervised trainingcited by 403
  6. 2017Cascaded Pyramid Network for Multi-Person Pose Estimationcited by 341
  7. 20213D Human Pose Estimation with Spatial and Temporal Transformerscited by 272
  8. 2019Semantic Graph Convolutional Networks for 3D Human Pose Regressioncited by 204
  9. 2022MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Videocited by 159
  10. 2021MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimationcited by 151
  11. 2021Graph Stacked Hourglass Networks for 3D Human Pose Estimationcited by 95
  12. 2020SRNet: Improving Generalization in 3D Human Pose Estimation with a Split-and-Recombine Approachcited by 94
4 more
  1. 2021Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimationcited by 94
  2. 2022P-STMO: Pre-Trained Spatial Temporal Many-to-One Model for 3D Human Pose Estimationcited by 80
  3. 2022GraphMLP: A Graph MLP-Like Architecture for 3D Human Pose Estimationcited by 21
  4. 2022Jointformer: Single-Frame Lifting Transformer with Error Prediction and Refinement for 3D Human Pose Estimationcited by 10

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