Reproduce “Pose Magic: Efficient and Temporally Consistent Human Pose Estimation with a Hybrid Mamba-GCN Network” on OpenResearch
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About this paper
Xinyi Zhang, Qiqi Bao, Qinpeng Cui, Wenming Yang, Qingmin Liao
Researchers at Tsinghua University developed Pose Magic, a hybrid Mamba-GCN network that establishes a new state-of-the-art in 3D human pose estimation with a 37.5mm MPJPE on Human3.6M while simultaneously reducing computational FLOPs by 74.1% compared to prior Transformer-based methods, and also demonstrates improved temporal consistency and generalization across sequence lengths.
Key insights
- Combining State Space Models (Mamba) for efficient long-range context and Graph Convolutional Networks (GCN) for local spatial-temporal dependencies effectively overcomes the accuracy-efficiency trade-off.
- Mamba's inherent continuous-time formulation naturally promotes superior temporal consistency in predicted 3D human motions.
- Parallel integration and adaptive fusion of Mamba and GCN streams are crucial for optimal performance, outperforming sequential integration approaches.
Summary by alphaXiv.
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