Realistic Face Reenactment via Self-Supervised Disentangling of Identity and Pose
Authors: Xianfang Zeng, Yusu Pan, Mengmeng Wang, Jiangning Zhang, Yong Liu12757-12764
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our model on Vox Celeb1 and Ra FD dataset. Experiment results demonstrate the superior quality of reenacted images and the flexibility of transferring facial movements between identities. |
| Researcher Affiliation | Academia | Institute of Cyber-Systems and Control, Zhejiang University, China {zzlongjuanfeng, corenel, mengmengwang, 186368}@zju.edu.cn, yongliu@iipc.zju.edu.cn |
| Pseudocode | No | No pseudocode or algorithm block is explicitly labeled or presented in a structured format. |
| Open Source Code | No | No explicit statement about releasing source code or a link to a code repository is provided in the paper. |
| Open Datasets | Yes | We evaluate our model on Vox Celeb1 and Ra FD dataset. [...] In experiments, quantitative and qualitative comparisons are conducted on Vox Celeb1 and Ra FD dataset (Nagrani, Chung, and Zisserman 2017; Zhang et al. 2019). |
| Dataset Splits | Yes | We train all the models on the training and validation set and report their results on the corresponding test set. [...] Specifically, we randomly select 50 videos from the test set and 32 hold-out frames from each video. These frames are excluded from the fine-tuning process (if necessary) and used as driving images to be transformed from the remaining part in each video. |
| Hardware Specification | Yes | All experiments are conducted in a node with 2 NVIDIA RTX 2080Ti GPUs. |
| Software Dependencies | No | The paper mentions using the Adam optimizer but does not specify version numbers for any programming languages, libraries, or other software components used in the implementation. |
| Experiment Setup | Yes | The learning rate is set to 1 10 4, except for the discriminator, whose is 4 10 4. We use the Adam (Kingma and Ba 2015) optimizer with β1 = 0, β2 = 0.9 and decrease learning rate linearly. [...] We experimentally determine to only optimize the two embedders in the first 30 epochs. |