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..
Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation
Authors: Lijie Fan, Wenbing Huang, Chuang Gan, Junzhou Huang, Boqing Gong3510-3517
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments and user studies verify the effectiveness of our approach. |
| Researcher Affiliation | Collaboration | Lijie Fan,1 Massachusetts Institute of Technology, 2Tencent AI Lab, 3MIT-Watson Lab EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain a block labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper does not provide any specific statement or link indicating that the source code for their methodology is publicly available. |
| Open Datasets | Yes | We not only use the public CK+ (Lucey et al. 2010) dataset for model training but also significantly extend it in scale. The new larger-scale dataset is named CK++. |
| Dataset Splits | Yes | We use 10 video clips from the CK++ dataset for validation and all the others for training. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Dlib Library (King 2009)' but does not specify its version number or versions for any other software dependencies. |
| Experiment Setup | Yes | The Adam optimizer is used in the experiments, with the initial learning rate of 0.0002. The whole training process takes 2100 epochs, where one epoch means a complete pass over the training data. All images are resized to 289x289 and randomly cropped to 256x256 before being fed into the network. We set the small increment to a = 0.1 for temporal regulation Rt. |