Full-Body Motion Reconstruction with Sparse Sensing from Graph Perspective
Authors: Feiyu Yao, Zongkai Wu, Li Yi
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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. |