Papers › Exploiting temporal information for 3D pose estimation
Exploiting temporal information for 3D pose estimation
Mir Rayat Imtiaz Hossain, James J. Little
In this work, we address the problem of 3D human pose estimation from a sequence of 2D human poses. Although the recent success of deep networks has led many state-of-the-art methods for 3D pose estimation to train deep networks end-to-end to predict from images directly, the top-performing approaches have shown the effectiveness of dividing the task of 3D pose estimation into two steps: using a state-of-the-art 2D pose estimator to estimate the 2D pose from images and then mapping them into 3D space. They also showed that a low-dimensional representation like 2D locations of a set of joints can be discriminative enough to estimate 3D pose with high accuracy. However, estimation of 3D pose for individual frames leads to temporally incoherent estimates due to independent error in each frame causing jitter. Therefore, in this work we utilize the temporal information across a sequence of 2D joint locations to estimate a sequence of 3D poses. We designed a sequence-to-sequence network composed of layer-normalized LSTM units with shortcut connections connecting the input to the output on the decoder side and imposed temporal smoothness constraint during training. We found that the knowledge of temporal consistency improves the best reported result on Human3.6M dataset by approximately 12.2% and helps our network to recover temporally consistent 3D poses over a sequence of images even when the 2D pose detector fails.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Human Pose Estimation | Human3.6M | Sequence-to-sequence network | Average MPJPE (mm) | 58.5 | #81 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | Sequence-to-sequence network | Multi-View or Monocular | Monocular | #81 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | Human3.6M | Sequence-to-sequence network | Using 2D ground-truth joints | No | #81 of 88 | Archive leaderboard | report |
| 3D Human Pose Estimation | HumanEva-I | Sequence-to-sequence network | Mean Reconstruction Error (mm) | 22 | #14 of 31 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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