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 [1].
MeInGame: Create a Game Character Face from a Single Portrait
Authors: Jiangke Lin, Yi Yuan, Zhengxia Zou311-319
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our method outperforms state-of-the-art methods used in games. |
| Researcher Affiliation | Collaboration | Jiangke Lin, 1 Yi Yuan, 1* Zhengxia Zou 2 1 Netease Fuxi AI Lab 2 University of Michigan EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes its method steps but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and dataset are available at https://github.com/Fuxi CV/Me In Game. |
| Open Datasets | Yes | We use the Celeb A-HQ dataset (Karras et al. 2017) to create our dataset. [...] The dataset we created consists of six subsets: {Caucasian, Asian and African} {female and male}. Each subset contains 400 texture maps. [...] Code and dataset are available at https://github.com/Fuxi CV/Me In Game. |
| Dataset Splits | Yes | From each subset, we randomly select 300 for training, 50 for evaluation, and 50 for testing. |
| Hardware Specification | Yes | We run our experiments on an Intel i7 CPU and an NVIDIA 1080Ti GPU, with Py Torch3D (v0.2.0) and its dependencies. |
| Software Dependencies | Yes | We run our experiments on an Intel i7 CPU and an NVIDIA 1080Ti GPU, with Py Torch3D (v0.2.0) and its dependencies. |
| Experiment Setup | Yes | The learning rate is set to 0.0001, we use the Adam optimizer and train our networks for 50 epochs. [...] The weights of loss terms are finally set as follows: λl1 = 3, λperc = 1, λsty = 1, λsym = 0.1, λstd = 3, λadv = 0.001. |