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We propose an end-to-end pipeline for both building and tracking 3D facialmodels from personalized in-the-wild (cellphone, webcam, youtube clips, etc.)video data. First, we present a method for automatic data curation andretrieval based on a hierarchical clustering framework typical of collisiondetection algorithms in traditional computer graphics pipelines. Subsequently,we utilize synthetic turntables and leverage deepfake technology in order tobuild a synthetic multi-view stereo pipeline for appearance capture that isrobust to imperfect synthetic geometry and image misalignment. The resultingmodel is fit with an animation rig, which is then used to track facialperformances. Notably, our novel use of deepfake technology enables us toperform robust tracking of in-the-wild data using differentiable renderersdespite a significant synthetic-to-real domain gap. Finally, we outline how wetrain a motion capture regressor, leveraging the aforementioned techniques toavoid the need for real-world ground truth data and/or a high-end calibratedcamera capture setup.

Leveraging Deepfakes to Close the Domain Gap between Real and Synthetic Images in Facial Capture Pipelines