HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping
Authors: Yuhan Wang, Xu Chen, Junwei Zhu, Wenqing Chu, Ying Tai, Chengjie Wang, Jilin Li, Yongjian Wu, Feiyue Huang, Rongrong Ji
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments |
| Researcher Affiliation | Collaboration | 1Youtu Lab, Tencent 2Zhejiang University 3Media Analytics and Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University 4Institute of Artificial Intelligence, Xiamen University |
| Pseudocode | No | The paper describes methods and equations, but does not include a dedicated pseudocode or algorithm block. |
| Open Source Code | Yes | Code is available at: https://johann.wang/HifiFace. |
| Open Datasets | Yes | We choose VGGFace2 [Cao et al., 2018] and Asian-Celeb [Deep Glint, 2020] as the training set. |
| Dataset Splits | No | We choose VGGFace2 [Cao et al., 2018] and Asian-Celeb [Deep Glint, 2020] as the training set. For our model with resolution 256 (i.e., Ours-256), we remove images with either size smaller than 256 for better image quality. The ratio of training pairs with the same identity is 50%. The test set contains 10K swapped faces and 10K real faces from FF++ for each method. |
| Hardware Specification | Yes | The model is trained with 200K steps, using 4 V100 GPUs and 32 batch size. |
| Software Dependencies | No | The paper mentions various models and algorithms by their citations (e.g., ADAM [Kingma and Ba, 2014], res-blocks [He et al., 2016], 3D face reconstruction model [Deng et al., 2019]), but does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | ADAM [Kingma and Ba, 2014] is used with β1 = 0; β2 = 0.99 and learning rate = 0.0001. The model is trained with 200K steps, using 4 V100 GPUs and 32 batch size. ... Lsid = λshape Lshape + λid Lid, where λid = 5 and λshape = 0.5. ... Lreal = Ladv + λ0Lseg + λ1Lrec + λ2Lcyc + λ3Llpips, where λ0 = 100, λ1 = 20, λ2 = 1 and λ3 = 5. |