Research map / 3D vision / Human pose estimation
Deep Network for the Integrated 3D Sensing of Multiple People in Natural Images
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| attention-based 3d decoding instead of mean decoding (ablation) | MubyNet · Human80K | MPJPE: 59.31 -> 58.40 | Helped |
| fine-tune 3d component on CMU Panoptic multi-person data | MubyNet · CMU Panoptic | MPJPE: 150.3 -> 72.1 | Helped |
| MubyNet bottom-up integrated model vs prior methods | multi-person 3d pose · Human3.6M | MPJPE: 73 -> 60 | Helped |
Closest papers by meaning
- Monocular 3D Pose and Shape Estimation of Multiple People in Natural Scenes - The Importance of Multiple Scene Constraints (CVPR 2018 · similarity 0.91)
- Deep Multitask Architecture for Integrated 2D and 3D Human Sensing (CVPR 2017 · similarity 0.89)
- Generating Multiple Hypotheses for 3D Human Pose Estimation With Mixture Density Network (CVPR 2019 · similarity 0.88)
- MUC: Mixture of Uncalibrated Cameras for Robust 3D Human Body Reconstruction (AAAI 2025 · similarity 0.87)
- Neural Localizer Fields for Continuous 3D Human Pose and Shape Estimation (NeurIPS 2024 · similarity 0.87)
- MultiPly: Reconstruction of Multiple People from Monocular Video in the Wild (CVPR 2024 · similarity 0.87)
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