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..
Learning Motion Refinement for Unsupervised Face Animation
Authors: Jiale Tao, Shuhang Gu, Wen Li, Lixin Duan
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on widely used benchmarks demonstrate that our method achieves the best results among state-of-the-art baselines. |
| Researcher Affiliation | Academia | School of Computer Science and Engineering, UESTC1 Shenzhen Institute for Advanced Study, UESTC2 |
| Pseudocode | No | The paper describes its method through text and equations but does not include structured pseudocode or an algorithm block. |
| Open Source Code | Yes | Codes will be available at https://github.com/Jiale Tao/MRFA/ |
| Open Datasets | Yes | We conduct experiments on the widely used Voxceleb1 [18] dataset and the recently collected more challenged Celeb V-HQ dataset [44]. |
| Dataset Splits | No | Voxceleb1 is a talking head dataset consisting of 20047 videos, among which 19522 are used for training and 525 are used for testing. |
| Hardware Specification | Yes | We train our method for 100 epochs on four NVIDIA A100 GPU cards or eight NVIDIA 3090 GPU cards. |
| Software Dependencies | No | The paper mentions optimizers (Adam) and networks (VGG-19, Unet) but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | We train our method for 100 epochs... The number of keyoints is set to 10... The patch radius r is set to 3 and the number of iterations is set to 6. The Adam optimizer [16] is adopted with β1 = 0.5 and β2 = 0.999, the initial learning rate is set as 2 * 10^-4 and dropped by a factor of 10 at the end of 60th and 90th epoch. |