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..
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. |