We present Capturing the Unseen (CAPUS), a novel facial motion capture (MoCap) technique that operates without visual signals. CAPUS leverages miniaturized Inertial Measurement Units (IMUs) as a new sensing modality for facial motion capture. While IMUs have become essential in full-body MoCap for their portability and independence from environmental conditions, their application in facial MoCap remains underexplored. We address this by customizing micro-IMUs, small enough to be placed on the face, and strategically positioning them in alignment with key facial muscles to capture expression dynamics. CAPUS introduces the first facial IMU dataset, encompassing both IMU and visual signals from participants engaged in diverse activities such as multilingual speech, facial expressions, and emotionally intoned auditions. We train a Transformer Diffusion-based neural network to infer Blendshape parameters directly from IMU data. Our experimental results demonstrate that CAPUS reliably captures facial motion in conditions where visual-based methods struggle, including facial occlusions, rapid movements, and low-light environments. Additionally, by eliminating the need for visual inputs, CAPUS offers enhanced privacy protection, making it a robust solution for various applications.

Abstract The development of neural relighting techniques has by far outpaced the rate of their corresponding training data (e.g., OLAT) generation. For example, high-quality relighting from a single portrait image still requires supervision from comprehensive datasets covering broad diversities in gender, race, complexion, and facial geometry. We present a hybrid parametric neural relighting (PN-Relighting) framework for single portrait relighting, using a much smaller OLAT dataset or SMOLAT. At the core of PN-Relighting, we employ parametric 3D faces coupled with appearance inference and implicit material modelling to enrich SMOLAT for handling in-the-wild images. Specifically, we tailor an appearance inference module to generate detailed geometry and albedo on top of the parametric face and develop a neural rendering module to first construct an implicit material representation from SMOLAT and then conduct self-supervised training on in-the-wild image datasets. Comprehensive experiments show that PN-Relighting produces comparable high-quality relighting to TotalRelighting (Pandey et al., 2021), but with a smaller dataset. It further improves shape estimation and naturally supports free-viewpoint rendering and partial skin material editing. PN-Relighting also serves as a data augmenter to produce rich OLAT datasets beyond the original capture.