Dual-Diffusion for Binocular 3D Human Pose Estimation
Authors: Xiaoyue Wan, Zhuo Chen, Bingzhi Duan, Xu Zhao
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments validate the effectiveness of our Dual-Diffusion in 2D refinement and 3D estimation. and To validate the efficacy of our Dual-Diffusion model in denoising both 2D and 3D poses, we conducted experiments on the binocular H36M [13] and MHAD [27] datasets, utilizing only 2-view camera pairs. |
| Researcher Affiliation | Academia | Xiaoyue Wan Zhuo Chen Bingzhi Duan Xu Zhao Department of Automation Shanghai Jiao Tong University {sherrywaan, chzh9311, Duan Bingzhi, zhaoxu}@sjtu.edu.cn |
| Pseudocode | No | The paper uses figures like Figure 2 and Figure 9 to illustrate the model and training process, but these are high-level diagrams and flowcharts, not detailed pseudocode or algorithm blocks. The procedural steps are described in paragraph text within the 'Training Details' and 'Inference' sections. |
| Open Source Code | Yes | The code and models are available at https://github.com/sherrywan/Dual-Diffusion. |
| Open Datasets | Yes | We conducted experiments on the binocular H36M [13] and MHAD [27] datasets, utilizing only 2-view camera pairs. |
| Dataset Splits | No | The paper mentions training on MHAD and H36M and evaluating on testing sets, but it does not specify explicit train/validation/test splits, such as percentages or sample counts for each subset, nor does it refer to predefined standard splits with citations. |
| Hardware Specification | Yes | All experiments are conducted on Ge Force RTX 2080 Ti. |
| Software Dependencies | No | The Dual-Diffusion is implemented by Py Torch [28] using Adam optimizer with learning rate 0.00002 and other parameters are all default. However, a specific version number for PyTorch is not provided. |
| Experiment Setup | Yes | The Dual-Diffusion is implemented by Py Torch [28] using Adam optimizer with learning rate 0.00002 and other parameters are all default. and For the experiments described below, we set T = 25 and K = 1 according to the ablation study in Sec. 4.3 and Appendix D.3. and We train our Dual-Diffusion in MHAD [27] for 80 epochs, and in H36M [13] for 40 epochs, respectively. |