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 [1].
Physics-Guided Human Motion Capture with Pose Probability Modeling
Authors: Jingyi Ju, Buzhen Huang, Chen Zhu, Zhihao Li, Yangang Wang
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our method outperforms previous physics-based methods in both joint accuracy and success rate. |
| Researcher Affiliation | Collaboration | Jingyi Ju1,2 , Buzhen Huang1,2 , Chen Zhu1,2 , Zhihao Li3 and Yangang Wang1,2 1Southeast University 2Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Nanjing, China 3Huawei Noah s Ark Lab EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | More information can be found at https://github. com/Me-Ditto/Physics-Guided-Mocap. |
| Open Datasets | Yes | Human3.6M [Ionescu et al., 2013] is an indoor dataset for human motion capture... 3DOH [Zhang et al., 2020] is the ο¬rst dataset to handle the object occluded human body estimation... MPI-INF-3DHP [Mehta et al., 2017] is a single-person 3D pose dataset. |
| Dataset Splits | Yes | Following previous works [Yuan et al., 2021; Shimada et al., 2020], we use 2 subjects (S9, S11) for evaluation, and the others are used for training. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Mu Jo Co' and 'SMPL model' but does not provide specific version numbers for these or other key software components or libraries. |
| Experiment Setup | No | The paper describes the overall training procedure and loss functions used, but does not provide specific experimental setup details such as concrete hyperparameter values (e.g., learning rate, batch size, number of epochs) or optimizer settings. |