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.

High quality and high speed videography using Non-Line-of-Sight (NLOS) imaging benefit autonomous navigation, collision prevention, and post-disaster search and rescue tasks. Current solutions have to balance between the frame rate and image quality. High frame rates, for example, can be achieved by reducing either per-point scanning time or scanning density, but at the cost of lowering the information density at individual frames. Fast scanning process further reduces the signal-to-noise ratio and different scanning systems exhibit different distortion characteristics. In this work, we design and employ a new Transient Transformer architecture called TransiT to achieve real-time NLOS recovery under fast scans. TransiT directly compresses the temporal dimension of input transients to extract features, reducing computation costs and meeting high frame rate requirements. It further adopts a feature fusion mechanism as well as employs a spatial-temporal Transformer to help capture features of NLOS transient videos. Moreover, TransiT applies transfer learning to bridge the gap between synthetic and real-measured data. In real experiments, TransiT manages to reconstruct from sparse transients of $16 \times 16$ measured at an exposure time of 0.4 ms per point to NLOS videos at a $64 \times 64$ resolution at 10 frames per second. We will make our code and dataset available to the community.

We introduce HOLI-1-to-3, a novel technique for holistic 3D shape recovery from a single-viewpoint input, by effectively combining line-of-sight (LOS) and non-line-of-sight (NLOS) imaging. We leverage advancements in ultrafast time-of-flight (ToF) sensors and learning-based 3D shape inference techniques, such as diffusion models. HOLI-1-to-3 employs a new neural plenoptic representation, which unifies radiance fields (for LOS RGB images) and transient fields (for NLOS transients). HOLI-1-to-3 is optimized through a two-stage pipeline involving diffusion priors and transients prior. Our technique allows for accurate and continuous reconstruction of both visible and invisible parts of objects from a single view. Comprehensive experiments on both simulated and real-world datasets demonstrate the effectiveness of HOLI-1-to-3in resolving ambiguities in invisible parts of objects and significantly improving overall generation quality. The datasets used in our experiments will be made available to the research community to facilitate further achievements in holistic 3D shape recovery.