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..
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 | Venue PDF | 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 EMAIL, EMAIL |
| 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. |