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..
Full-Body Motion Reconstruction with Sparse Sensing from Graph Perspective
Authors: Feiyu Yao, Zongkai Wu, Li Yi
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method s effectiveness is evidenced by the attained state-of-the-art performance, particularly in lower body motion, outperforming other baseline methods. Additionally, an ablation study validates the efficacy of each module in our proposed framework. |
| Researcher Affiliation | Collaboration | Feiyu Yao1, Zongkai Wu2*, Li Yi3,4,5* 12012 Lab, Huawei Technologies Co., Ltd 2Fancy Technology 3Tsinghua University 4Shanghai Artificial Intelligence Laboratory 5Shanghai Qi Zhi Institute |
| Pseudocode | No | The paper does not contain structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement or a link to the open-source code for the described methodology. |
| Open Datasets | Yes | CMU (Lab 2000), BMLrub (Troje 2002) and HDM05 (M uller et al. 2007) in AMASS (Mahmood et al. 2019) dataset are employed. |
| Dataset Splits | No | The datasets are randomly partitioned into training and testing subsets, comprising 90% and 10% of the data respectively, following the same setting as (Jiang et al. 2022). The paper does not explicitly mention a separate validation dataset split with specific percentages or counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiments. |
| Experiment Setup | No | The paper discusses the model architecture and loss function but does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size) or training configurations. |