Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Sequential Joint Dependency Aware Human Pose Estimation with State Space Model
Authors: Hanxi Yin, Shaodi You, Jungong Han, Zhixiang Chen
AAAI 2025 | Venue PDF | 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. |