MetaHumans Help to Evaluate Deepfake Generators
Sahar Husseini, Jean‐Luc Dugelay · 2023
The progress achieved in deepfake technology has been remarkable; however, evaluating the resulting videos and comparing different generators remains challenging. A primary concern arises from the lack of ground-truth data, except for self-reenactment scenarios. Additionally, available datasets may have inherent limitations, such as lacking expected animations or demonstrating inadequate subject diversity. Furthermore, there are ethical and privacy concerns when using real individuals' faces in such applications. This paper goes beyond the state-of-the-art dealing with the evaluation of deepfake generators by introducing an innovative dataset featuring MetaHumans. Our dataset ensures the availability of ground-truth data and encompasses diverse facial expressions, variations in pose and illumination conditions, and combinations of these factors. Additionally, we meticulously control and verify the expected animations within the dataset. The proposed dataset enables accurate evaluation of cross-reenactment generated images. By utilizing various established metrics, we demonstrate a high degree of correlation between the generator's scores obtained from deep-fake videos of Metahumans and those obtained from deepfake videos of real persons. The synthesized MetaHuman dataset can be accessed at: https://github.com/SaharHusseini/MMSP_2023