Learning Motion Refinement for Unsupervised Face Animation
Authors: Jiale Tao, Shuhang Gu, Wen Li, Lixin Duan
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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. |