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 | Conference PDF | Archive PDF | Plain Text | 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. |