LogInThe method allows for the preservation of facial identity across variable expressions, viewpoints, and lighting conditions.
The Disney Research team has presented FRAN, a fully-automatic production-ready face re-aging neural network capable of aging and de-aging human faces in videos without identity loss across variable expressions, viewpoints, and lighting conditions.
Trained on a dataset of photo-realistically re-aged, synthetic face pairs, the network provides temporally stable results on videos and incorporates simple and intuitive mechanisms that enable the artist to tweak and customize the re-aged results. According to the team, the network "reformulates re-aging as a simple image-to-image translation task that is naturally and effectively solved using the familiar U-Net architecture."
"Our first key insight is in addressing the problem of collecting longitudinal training data for learning to re-age faces over extended periods of time, a task that is nearly impossible to accomplish for a large number of real people. We show how such a longitudinal dataset can be constructed by leveraging the current state-of-the-art in facial re-aging that, although failing on real images, does provide photoreal re-aging results on synthetic faces," commented the team. "Our second key insight is then to leverage such synthetic data and formulate facial re-aging as a practical image-to-image translation task that can be performed by training a well-understood U-Net architecture, without the need for more complex network designs."
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