Research map / Graph learning / Knowledge and temporal graphs
Semantic Graph Convolutional Networks for 3D Human Pose Regression
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add SemGConv to the ResGCN baseline (ablation) | ResGCN · Human3.6M, 2D GT | MPJPE (mm): 94.4 -> 65.9 | Helped |
| Add non-local layers to SemGCN with SemGConv (ablation) | SemGCN · Human3.6M, 2D GT | MPJPE (mm): 52.5 -> 43.8 | Helped |
| Add SemGConv to a model with non-local layers (ablation) | SemGCN · Human3.6M, 2D GT | MPJPE (mm): 65.9 -> 43.8 | Helped |
| Add pooled ResNet50 image features to 2D detections | SemGCN · Human3.6M Protocol #1 | MPJPE (mm): 60.8 -> 57.6 | Helped |
| Refine SemGCN predictions with PG | SemGCN · Human3.6M, 2D GT | MPJPE (mm): 43.8 -> 42.5 | Helped |
| Use max pooling to reduce graph nodes in non-local layers | SemGCN · Human3.6M | MPJPE (mm) | No clear effect |
Closest papers by meaning
- Deep Semantic Graph Transformer for Multi-View 3D Human Pose Estimation (AAAI 2024 · similarity 0.88)
- 3D Human Pose Estimation From a Single Image via Distance Matrix Regression (CVPR 2017 · similarity 0.88)
- Convolutional Mesh Regression for Single-Image Human Shape Reconstruction (CVPR 2019 · similarity 0.88)
- GCNext: Towards the Unity of Graph Convolutions for Human Motion Prediction (AAAI 2024 · similarity 0.86)
- GRPose: Learning Graph Relations for Human Image Generation with Pose Priors (AAAI 2025 · similarity 0.86)
- Dynamic Semantic-Based Spatial Graph Convolution Network for Skeleton-Based Human Action Recognition (AAAI 2024 · similarity 0.86)
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