# Generative Adversarial Network for Overcoming Occlusion in Images: A Survey
## 5 GAN in Amodal Completion
### Amodal Appearance Reconstruction
#### 5.3.2 Face Completion
The second stage eliminates the mask object and regenerates the corrupted pixels through two pair of generators and discriminators. The occludedinput image and the extracted occlusion mask are fed into the first generator to produce a completed image. The initial output from the first generator is enhanced by rectifying any missing or incorrect content in it. Two PatchGAN discriminators are implemented against the result of the generators to ensure that the restored face's appearance and structural consistency are retained. AFD-StackGAN can remove various types of occlusion masks in the facial images that cover a large area of the face. However, it is trained with synthetic data, and the incompatibility of the training images and the real-world testing images is likely. In the same way, [PERSON] et al. [90] employ two generators and three domain-specific discriminators in their proposed framework called disentangling and fusing GAN (DF-GAN). They treat face completion as disentangling and fusing of clean faces and occlusions. This way, they remove the need for paired samples of occluded images and their congruent clean images. The framework works with three domains that correspond to the distribution of occluded faces, clean faces, and structured occlusions. In the disentangling module, an occluded facial image is fed into an encoder which encodes it to the disentangled representations. Thereafter, two decoders produce the corresponding deoccluded image and occlusion, respectively. In other words, the disentangling network learns how to separate the structured occlusions and the occlusion-free images. The fusing network, on the other hand, combines the latent representations of clean faces and occlusions, and creates the corresponding occluded facial image, i.e., it learns how to generate images with structured occlusions. However, real-world occlusions are of arbitrary shape and size, not necessarily structured. **Coarse-to-fine architecture:** Conversely to the previously mentioned works where one output is generated, [PERSON] et al. [91] propose a two-stage Face De-occlusion using Stacked Generative Adversarial Network (FD-StackGAN) model that follows the coarse-to-fine approach. The model attempts to remove the occlusion mask and fill in the affected area. In the first stage, the network produces an initial deoccluded facial image. The second stage refines the initial generated image to create a more visually plausible image that is similar to the real image. Similar to AF-StackGAN, FD-StackGAN can handle various regions in the facial images with different structures and surrounding backgrounds. However, the model is trained on a synthetic dataset but it is not tested on images with natural occlusions. Likewise, [PERSON] and [PERSON] [92] address the problem of deoccluding and recognizing face profiles with large-pose variations and occlusions through BoostGAN, which has a coarse-to-fine structure. In the coarse part, i.e., multi-occlusion frontal view generator, an encoder-decoder network is used for eliminating occlusion and producing multiple intermediate deoccluded faces. Subsequently, the coarse outputs are refined through a boosting network for photo-realistic and identity-preserved face generation. Consequently, the discriminator has a multi-input structure. Since BoostGAN is a one-stage framework, it cannot handle de-occlusion and frontalization concurrently, which means that it loses the discriminative identity information. Furthermore, BoostGAN fails to employ the mask guided noise prior information. To address these, [PERSON] et al. [93] perform face frontalization and face completion simultaneously. They propose an end-to-end mask guided two-stage GAN (TSGAN) framework. Each stage has its own generator and discriminator, while the first stage contains the face deocclusion module, the second one contains face frontalization module. Another module named mask-attention module (MAM) is deployed in both stages.