FlowFace: Semantic Flow-Guided Shape-Aware Face Swapping
Authors: Hao Zeng, Wei Zhang, Changjie Fan, Tangjie Lv, Suzhen Wang, Zhimeng Zhang, Bowen Ma, Lincheng Li, Yu Ding, Xin Yu
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive quantitative and qualitative experiments on in-the-wild faces demonstrate that our Flow Face outperforms the state-of-the-art significantly. |
| Researcher Affiliation | Collaboration | Hao Zeng1, Wei Zhang1, Changjie Fan1, Tangjie Lv1, Suzhen Wang1, Zhimeng Zhang1, Bowen Ma1, Lincheng Li1, Yu Ding1,3,*, Xin Yu2 1Virtual Human Group, Netease Fuxi AI Lab 2University of Technology Sydney 3Zhejiang University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | More details are in the supplementary materials and our codes will be made publicly available upon publication of the paper. |
| Open Datasets | Yes | The training dataset is collected from three commonly-used face datasets: Celeb A-HQ (Karras et al. 2017), FFHQ (Karras, Laine, and Aila 2019), and VGGFace2 (Cao et al. 2018). |
| Dataset Splits | Yes | The final dataset contains 350K face images, and 10K images are randomly sampled as the validation dataset. For the comparison experiments, we construct the test set by sampling Face Forensics++(FF++) (R ossler et al. 2019), following (Li et al. 2019). Specifically, FF++ consists of 1000 video clips, and the test set is collected by sampling ten frames from each clip of FF++, in a total of 10000 images. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions software components like Adam optimizer, pre-trained face recognition models, and VGG, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Our Flow Face is trained in a two-stage manner. Specifically, F res is first trained for 32K steps with a batch size of eight. As for F swa, we first pre-trained the face encoder following the training strategy of MAE on our face dataset. Then we fix the encoder and train other components of F swa for 640K steps with a batch size of eight. We adopt Adam (Kingma and Ba 2014) optimizer with β1=0 and β2=0.99 and the learning rate is set to 0.0001. |