# Appearance-Based Gaze Estimation as a Benchmark for Eye Image Data Generation Methods
## 7 Discussion and Future Work  
To the best of our knowledge, the StarGANv2-SPADE+AdaIN method is the first method to generate realistic eye image data that does not require ground-truth supervision during training, as in [53]; a costly data collection setup akin to [68]; and can be used for any appearance-based research, unlike [57] designed purely for gaze redirection. The effectiveness of our method is proved by a novel benchmark that directly evaluates any synthetic data source as a tool for data augmentation. We hope that the proposed data augmentation benchmark will be valuable to the eye-tracking community, as it is easily extensible to other evaluation scenarios. For instance, it is straightforward to adapt it for the task of zero- or few-shot gaze estimation. While StarGANv2-SPADE+AdaIN showed only a mild mean gaze estimation improvement of 11.22%, the average size of the sparse training set of real images used as a baseline was around 195 samples. It is reasonable to assume that the smaller this set is, the larger the improvement would be. Even if no subject-specific data are available for training, the task of zero-shot gaze estimation may be solved by using data from many synthetic subject identities. Any of these hypotheses can be formally tested within essentially the same evaluation framework, with the only minor change in the size of real data available for training. To generate data for a novel identity, we may need as few as just one image to compute the rough estimate of its style latent code. The sensitivity of data augmentation performance to the variance in the style code estimation is another question that we leave for future work. The data augmentation study evaluates the mismatch between the real and generated data based on the image features that the neural network learned to be useful for gaze estimation. As in a typical fashion of end-to-end learning from data, the resulting features are not easily mapped to tangible domain-specific concepts. The fact that these features are not human explainable makes it hard to derive a plan for addressing the large remaining gap between real and fake images in appearance-based ET research. However, it is reasonable to assume that some linear combination of the general metrics in Table 1 may be a good predictor of the data augmentation performance of a given method. Thinking in this framework makes future work planning more feasible, as the goal of improving any objective well-defined metric is straightforward to set. Unfortunately, it is not clear from our study alone to what extent FID, mIoU, or style control influence the data augmentation  
Figure 5: Comparison of data augmentation performance between train and test subjects for Blender and StarGANv2-SPADE+AdaIN methods. The plot format follows Figure 4.  
performance, yet alone which metric is the most contributing factor in the performance increase. The Blender had an inferior FID score to any EyeGAN-based method, but it beat both of them in data augmentation. EyeGAN-RITNet had the best image content consistency among learning-from-data methods, yet it was easily beaten by StarGANv2-SPADE+AdaIN with one of the worst mIoUs. The only limited evidence we have is that increasing the mIoU within a similar range of FID and the same style-control ability leads to improved performance in data augmentation. This happened to be the case for our proposed modifications to both the original EyeGAN and StarGANv2 methods that increased their baseline mIoU level. Therefore, we suggest that further improvement in content consistency while maintaining or improving photorealism in StarGANv2-SPADE+AdaIN generated images is the most promising step for future work. To sum up, this work presented a benchmark of eye image generation methods. We tested their applicability in improving appearance-based gaze estimation through data augmentation. In that scenario, the generated samples were mixed in with a sparse set of real data used to train a neural network inferring gaze from eye images in an end-to-end manner.