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..
Learning Basis Representation to Refine 3D Human Pose Estimations
Authors: Chunyu Wang, Haibo Qiu, Alan L. Yuille, Wenjun Zeng8925-8932
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on benchmark datasets show that our approach obtains more legitimate poses over the baselines. |
| Researcher Affiliation | Collaboration | Microsoft Research Asia, Beijing, China The Johns Hopkins University, Baltimore, MD 21218, USA |
| Pseudocode | No | No pseudocode or algorithm blocks are present. The methodology is described through text and mathematical formulations. |
| Open Source Code | No | No explicit statement about releasing their own source code or a link to it is provided. |
| Open Datasets | Yes | We evaluate our 3D pose refinement approach on two benchmark datasets: H36M (Ionescu et al. 2014) and MPIINF-3DHP (Mehta et al. 2017). |
| Dataset Splits | No | Following the most common evaluation protocol (Zhou et al. 2017; Pavlakos et al. 2017), we use five subjects (i.e. S1, S5, S6, S7, S8) for training and two subjects (S9, S11) for testing. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments are provided. |
| Software Dependencies | No | Only “Pytorch” is mentioned, but no specific version number or other software dependencies with their versions are listed. |
| Experiment Setup | Yes | We learn 1000 bases for all the training poses. |