MarioNETte: Few-Shot Face Reenactment Preserving Identity of Unseen Targets
Authors: Sungjoo Ha, Martin Kersner, Beomsu Kim, Seokjun Seo, Dongyoung Kim10893-10900
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments are performed to verify that the proposed framework can generate highly realistic faces, outperforming all other baselines, even under a significant mismatch of facial characteristics between the target and the driver. Our experiments including user studies show that the proposed method outperforms the state-of-the-art methods. |
| Researcher Affiliation | Industry | Sungjoo Ha, Martin Kersner, Beomsu Kim, Seokjun Seo, Dongyoung Kim Hyperconnect Seoul, Republic of Korea {shurain, martin.kersner, beomsu.kim, seokjun.seo, dongyoung.kim}@hpcnt.com |
| Pseudocode | No | The paper includes architectural diagrams and descriptions of methods but no structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides a URL for 'Additional example images and videos' (http://hyperconnect.github.io/MarioNETte) but does not explicitly state that the source code for the methodology described in the paper is available at this or any other location. |
| Open Datasets | Yes | We trained our model and the baselines using Vox Celeb1 (Nagrani, Chung, and Zisserman 2017), which contains 256 256 size videos of 1,251 different identities. We utilized the test split of Vox Celeb1 and Celeb V (Wu et al. 2018) for evaluating self-reenactment and reenactment under a different identity, respectively. |
| Dataset Splits | No | The paper specifies training and test datasets and how test sets were sampled, but does not explicitly provide details about a separate validation split or its purpose. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types) used to run its experiments. |
| Software Dependencies | No | The paper mentions some tools like Open Face and ResNet-50 but does not provide specific version numbers for any software dependencies or frameworks required to replicate the experiment. |
| Experiment Setup | No | The paper states that 'Further details of the loss function and the training method can be found at Supplementary Material A3 and A4,' implying that explicit experimental setup details like hyperparameters are not present in the main text. |