Semi-Dynamic Hypergraph Neural Network for 3D Pose Estimation
Authors: Shengyuan Liu, Pei Lv, Yuzhen Zhang, Jie Fu, Junjin Cheng, Wanqing Li, Bing Zhou, Mingliang Xu
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that the proposed method achieves state-of-the-art performance both on the Human3.6M and MPI-INF-3DHP datasets. |
| Researcher Affiliation | Academia | 1Center for Interdisciplinary Information Science Research, Zhengzhou University 2Advanced Multimedia Research Lab, University of Wollongong |
| Pseudocode | Yes | Algorithm 1: Dynamic Hypergraph Construction |
| Open Source Code | No | The paper does not provide any explicit statement about making the source code available or include a link to a code repository. |
| Open Datasets | Yes | We evaluate the proposed method on two datasets: Human3.6M and MPI-INF-3DHP. The Human3.6M is one of the largest datasets for 3D human pose estimation. It consists of 3.6 millions of images featuring 11 actors performing 15 daily activities, such as walking, eating, sitting and smoking with 4 camera views. |
| Dataset Splits | No | The paper specifies training and testing subjects for Human3.6M ('We use the subject 1, 5, 6, 7 and 8 in Human3.6m for training and subject 9 and 11 for testing.') but does not explicitly mention a validation set or cross-validation strategy. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Adam optimizer' and 'Re LU' and uses 'Stacked Hourglass network', but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | Our model is trained with Adam optimizer for 100 epochs, learning rate of 0.001 and batch size of 256. Re LU is chosen as the nonlinear activation function. The hidden dimension of our method is 256, which means the input data with the shape of (16,2) is mapped into a 256 dimension vector. |