FaceController: Controllable Attribute Editing for Face in the Wild
Authors: Zhiliang Xu, Xiyu Yu, Zhibin Hong, Zhen Zhu, Junyu Han, Jingtuo Liu, Errui Ding, Xiang Bai3083-3091
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive quantitative and qualitative evaluations have been conducted. In a single framework, our method achieves the best or competitive scores on a variety of face applications. Experiments Implementation details. The training images for Face Controller are collected from Celeb A-HQ (Karras et al. 2017), FFHQ (Karras, Laine, and Aila 2019), and VGGFace (Parkhi, Vedaldi, and Zisserman 2015) datasets. |
| Researcher Affiliation | Collaboration | 1 Huazhong University of Science and Technology 2 Baidu Inc. |
| Pseudocode | No | Not found. |
| Open Source Code | No | Not found. The paper only links to code for a comparison method (Face Swap) not their own. |
| Open Datasets | Yes | The training images for Face Controller are collected from Celeb A-HQ (Karras et al. 2017), FFHQ (Karras, Laine, and Aila 2019), and VGGFace (Parkhi, Vedaldi, and Zisserman 2015) datasets. |
| Dataset Splits | No | The paper uses some training data for face reconstruction (20%) but does not specify overall train/validation/test splits for model training, nor does it specify how the datasets mentioned (Celeb A-HQ, FFHQ, VGGFace) are split for training and validation. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models) are mentioned. |
| Software Dependencies | No | The paper mentions software like 'PyTorch' and 'Bi Se Net model' and 'VGG network' but does not specify their version numbers. |
| Experiment Setup | Yes | The generator and discriminator are trained around 500K steps, respectively. More details can be found in the Supplementary Materials. ... L = Ladv + λid Lid + λlm Llm + λhm Lhm + λper Lper, (9) where Ladv denotes GAN loss. We set the λid = 10, λlm = 10000, λhm = 100, and λper = 100, respectively. |