Research map / 3D vision / Human pose estimation
2D/3D Pose Estimation and Action Recognition Using Multitask Deep Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add estimated poses in place of Kinect poses (ablation) | Ours on NTU RGB+D · NTU RGB+D (cross-subject) | Accuracy (%): +2.9% | Helped |
| Fine-tune the full network end to end (ablation) | Ours on NTU RGB+D · NTU RGB+D (cross-subject) | Accuracy (%): +3.3% | Helped |
| Average predictions across multiple video clips (ablation) | Ours on NTU RGB+D · NTU RGB+D (cross-subject) | Accuracy (%): +1.1% | Helped |
| Train 3D pose estimation on Human3.6M + MPII instead of Human3.6M only (ablation) | Ours · Human3.6M | MPJPE (mm): 67.3 -> 55.1 | Helped |
| Use multi-crop evaluation with horizontal flips instead of single-crop evaluation (ablation) | Ours · Human3.6M | MPJPE (mm): 55.1 -> 53.2 | Helped |
| Use annotated poses instead of estimated poses (ablation) | Ours on Penn Action · Penn Action | Accuracy (%): 97.4 -> 98.6 | Helped |
| Train pose and action jointly rather than sequentially followed by fine-tuning (ablation) | Ours on Penn Action · Penn Action | Accuracy (%): 97.4 -> 97.3; 0.1% | Hurt |
Closest papers by meaning
- Deep Multitask Architecture for Integrated 2D and 3D Human Sensing (CVPR 2017 · similarity 0.89)
- PA3D: Pose-Action 3D Machine for Video Recognition (CVPR 2019 · similarity 0.89)
- Deep Analysis of CNN-Based Spatio-Temporal Representations for Action Recognition (CVPR 2021 · similarity 0.88)
- 3D Action Recognition From Novel Viewpoints (CVPR 2016 · similarity 0.88)
- Attention Mechanism Exploits Temporal Contexts: Real-Time 3D Human Pose Reconstruction (CVPR 2020 · similarity 0.88)
- 3D Human Pose Estimation = 2D Pose Estimation + Matching (CVPR 2017 · similarity 0.87)
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