Sequential Joint Dependency Aware Human Pose Estimation with State Space Model
Authors: Hanxi Yin, Shaodi You, Jungong Han, Zhixiang Chen
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on two datasets to validate the effectiveness of our proposed SSM module, and the results demonstrate that our pose estimator can deliver impressive performance. |
| Researcher Affiliation | Academia | 1University of Amsterdam 2University of Sheffield EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Joint-Dependent SSM Layer Input: Fx: (B, L, Ds) Output: Fy: (B, L, Ds) 1: A: (Ds, (N) Parameter Represents structured N N matrix 2: B: (L, N) Parameter 3: C: (L, N) Parameter 4: : (L, Ds) Softplus(Parameter) 5: A, B: (L, Ds, N) discretize( , A, B) 6: h: (B, Ds, N) initialize( B, Fx) with Eq. 2 7: Fy SSM( A, B, C)(Fx, h) with Eqs. 3 and 4 Joint-dependent 8: return Fy |
| Open Source Code | Yes | Code https://github.com/yinhanxi/Pose SSM |
| Open Datasets | Yes | Our method is evaluated on Human3.6M (Ionescu et al. 2014) and MPI-INF-3DHP (Mehta et al. 2017a). |
| Dataset Splits | Yes | Human3.6M. Following previous works (Gong, Zhang, and Feng 2021; Wandt and Rosenhahn 2019; Martinez et al. 2017; Zeng et al. 2020; Chen et al. 2018), we utilize subjects 1, 5, 6, 7 and 8 for training, and subjects 9 and 11 for evaluation. |
| Hardware Specification | Yes | Our method is implemented on a single NVIDIA Ge Force RTX 4090 GPU. |
| Software Dependencies | No | The paper mentions that the method is implemented on a single NVIDIA Ge Force RTX 4090 GPU but does not specify software dependencies like programming language versions or library versions (e.g., PyTorch version, CUDA version). |
| Experiment Setup | Yes | We train the model 30 epochs with a batch size of 512. The learning rate is initialized at 0.0005, decayed by 0.95 per epoch and halved every 5 epochs. Horizontal flip is applied as data augmentation in training. Experimentally, we set D, K and N as 160, 2 and 4 with the optimal MPJPE on Human3.6M by searching each parameter independently. We search D from 96 to 240, with a step size of 16, K from {2,4,8} and N from {2,4,8,16}. We initialize SSM parameters A and according to Mamba (Gu and Dao 2023), and randomly initialize B and C. |