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..
Copy Motion From One to Another: Fake Motion Video Generation
Authors: Zhenguang Liu, Sifan Wu, Chejian Xu, Xiang Wang, Lei Zhu, Shuang Wu, Fuli Feng
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that our method is able to generate realistic target person videos, faithfully copying complex motions from a source person. Our code and datasets are released at https://github.com/Sifann/Fake Motion |
| Researcher Affiliation | Academia | Zhenguang Liu1,2 , Sifan Wu2 , Chejian Xu1 , Xiang Wang3 , Lei Zhu4 , Shuang Wu5 and Fuli Feng6 1Zhejiang University 2Zhejiang Gongshang University 3National University of Singapore 4Shandong Normal Unversity 5Nanyang Technological University 6University of Science and Technology of China |
| Pseudocode | No | The paper includes architectural diagrams (Fig. 1, Fig. 2) and mathematical equations, but no structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code and datasets are released at https://github.com/Sifann/Fake Motion |
| Open Datasets | Yes | Experiments are conducted on two benchmark datasets, i PER [Liu et al., 2019a] and Complex Motion. ...Our code and datasets are released at https://github.com/Sifann/Fake Motion |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits or cross-validation methodology. It mentions using 'benchmark datasets' and 'mini-batch of 10 for 120 epochs' but no specific split details for validation. |
| Hardware Specification | Yes | We train our model with a mini-batch of 10 for 120 epochs on a Nvidia RTX 2080-Ti GPU. |
| Software Dependencies | No | The paper mentions 'Open Pose' and 'Mask-RCNN' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | During training, all the images are resized to 512 512. We train our model with a mini-batch of 10 for 120 epochs...The initial learning rate is set to 1e 4. We employ the Adam optimizer with β1 = 0.9 and β2 = 0.999. |