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..
MOST-GAN: 3D Morphable StyleGAN for Disentangled Face Image Manipulation
Authors: Safa C. Medin, Bernhard Egger, Anoop Cherian, Ye Wang, Joshua B. Tenenbaum, Xiaoming Liu, Tim K. Marks1962-1971
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we manipulate portrait images with respect to several physical attributes and compare them with the results of a state-of-the-art relighting method (Hou et al. 2021) and a state-of-the-art real-image manipulation method, PIE (Tewari et al. 2020b). Besides providing qualitative comparisons with these two methods, we also quantitatively compare the performance of our pose editing algorithm by employing a head pose estimator (Ruiz, Chong, and Rehg 2018) to measure the error between the desired and estimated head poses. |
| Researcher Affiliation | Collaboration | 1Mitsubishi Electric Research Laboratories (MERL) 2Massachusetts Institute of Technology 3Friedrich-Alexander-University Erlangen-Nuremberg 4Michigan State University |
| Pseudocode | No | The paper describes the architecture and process in narrative text and diagrams, but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the methodology described. |
| Open Datasets | Yes | After pretraining, we train our model using the FFHQ face dataset (Karras, Laine, and Aila 2019)... we extract hair masks from the USC Hair Salon database (Hu et al. 2015)... |
| Dataset Splits | No | The paper mentions training and testing on datasets but does not explicitly specify detailed training/validation/test splits (e.g., percentages or counts for each split, or references to how the FFHQ dataset was split into these portions for the authors' experiments). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper mentions using StyleGAN2 and MichiGAN but does not specify version numbers for these or any other software dependencies. |
| Experiment Setup | No | The paper describes the training process with loss functions but does not specify concrete hyperparameters like learning rate, batch size, or number of epochs for the experimental setup. |