With the proliferation of immersive Head-Mounted Displays (HMDs) for Virtual and Augmented Reality (VR/AR), reliable and high-precision eye tracking has become increasingly important. Conventional 2D image-based methods offer low system complexity but remain limited in stability, accuracy, and robustness. Three-dimensional ocular surface reconstruction can provide richer geomet-ric information, and structured light profilometry is particularly attractive because it enables dense and accurate surface measurement. However, Phase-Shifting Profilometry (PSP), which estimates phase from sequentially acquired fringe images, is highly susceptible to motion-induced errors when the eye rotates between frames. This study proposes a rotational motion compensation framework for PSP-based dynamic 3D eye reconstruction. Relative eye rotation is estimated from image-based motion cues using a user-specific 3D eye model in a spherical-coordinate domain. The estimated motion is then used to compensate for camera-pixel mismatch and phase-shift errors caused by inter-frame rotation. A region-wise optimization strategy is further introduced to reduce residual artifacts by inde-pendently refining the compensation strength in different ocular regions. Experiments with a rotating fake eye under non-uniform motion demonstrate that the proposed method substantially suppresses motion-induced deformation and improves reconstruction accuracy. An additional experiment with a non-spherical rigid object indicates that the compensation principle is not restricted to spherical eye geometry. These results establish a practical basis for stable PSP-based dynamic 3D eye reconstruction toward future high-precision eye tracking in immersive environments.
Antonio Ortiz-Gonzalez, Erich Kobler, Lukas Schletter +1cs.CV
Magnetic resonance imaging (MRI) is highly susceptible to patient motion due to its relatively long acquisition times and the fact that data are acquired sequentially in k-space. Even small patient movements introduce phase inconsistencies across measurements, leading to severe artifacts such as blurring, ghosting, and geometric distortions that can compromise diagnostic quality. Retrospective motion compensation remains challenging, particularly in accelerated acquisitions, due to the ill-posed nature of the joint reconstruction and motion estimation problem. In this work, we propose a unified Bayesian framework for motion-compensated 3D MRI that jointly estimates the anatomical image, rigid-body motion parameters, and coil sensitivity maps directly from motion-corrupted k-space data. Our approach integrates pretrained 3D complex-valued score-based diffusion models as expressive anatomical image priors within a physics-based forward model. Inference is performed by alternating diffusion posterior image updates with efficient proximal optimization steps for motion and coil sensitivity estimation, enabling fully unsupervised reconstruction without the need for paired motion-free training data. Experiments on simulated and real-motion brain MRI datasets demonstrate that the proposed method achieves improved image quality and motion robustness compared to state-of-the-art classical and learning-based motion correction techniques, particularly in the presence of severe motion and high acceleration.
Human Action Recognition (HAR) is a fundamental computer vision task with diverse real-world applications. Practical deployments often involve low-light environments and unconstrained 6-DoF camera motion, conditions that degrade visual quality, disrupt temporal coherence, and compromise reliability of existing methods. Event cameras, with high low-light sensitivity and microsecond-level temporal resolution, paired with an inertial measurement unit (IMU), present a promising solution. However, current research faces two key challenges: absence of a benchmark integrating low-light conditions, 6-DoF motion, and synchronized IMU data; and lack of effective motion compensation techniques. To address these, we propose Event-IMU Stabilized HAR (EIS-HAR), with two modules. The first is an EIS module that reduces motion blur via a non-linear warping function to reconstruct a motion-compensated input. The second is a HAR module with a four-stage hybrid architecture to efficiently extract spatiotemporal features for accurate action recognition. To alleviate data scarcity, we introduce DarkShake-DVS, the first large-scale event-based HAR benchmark that includes 18,041 realworld clips captured in low light and intense 6-DoF motion, supplemented by synchronized IMU data. Extensive experiments on three datasets demonstrate consistent superiority of EIS-HAR over state-of-the-art methods.