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].
Human Motion Generation via Cross-Space Constrained Sampling
Authors: Zhongyue Huang, Jingwei Xu, Bingbing Ni
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results show that the proposed framework successfully generates novel human motion sequences with reasonable visual quality. |
| Researcher Affiliation | Academia | Zhongyue Huang, Jingwei Xu and Bingbing Ni Shanghai Jiao Tong University, China EMAIL |
| Pseudocode | Yes | Algorithm 1 Optimization Algorithm |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code, nor does it include a link to a code repository. |
| Open Datasets | Yes | KTH Dataset. This dataset [Schuldt et al., 2004]... Human3.6M Dataset. This dataset [Ionescu et al., 2014] |
| Dataset Splits | No | The paper specifies training and testing splits (e.g., 'For KTH datasets, we use person 1-15 for training and 16-25 for testing'), but it does not explicitly mention a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU models, CPU types, or cloud instance specifications. |
| Software Dependencies | No | The paper mentions software components like 'Adam solver', 'Open Pose', and 'Res Net-18' but does not specify their version numbers, which are necessary for reproducible software dependencies. |
| Experiment Setup | Yes | All networks were trained using the Adam solver with a leanrning rate as 0.0001 and a batch size of 10. We set λ = γ = 10 and α = β = 1. |