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..
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. |