Hoijoon Jung

Mingxiao Tu, Hoijoon Jung, Alireza Moghadam et al.

Recovering a dense 3D body mesh from monocular video remains challenging under occlusion from draping and continuously moving camera viewpoints. This configuration arises in surgical augmented reality (AR), where an anesthetized patient lies under surgical draping while a surgeon's head-mounted camera continuously changes viewpoint. Existing human mesh recovery (HMR) methods are typically trained on upright, moving subjects captured from relatively stable cameras, leading to performance degradation under such conditions. To address this, we present Patient4D, a stationarity-constrained reconstruction pipeline that explicitly exploits the stationarity prior. The pipeline combines image-level foundation models for perception with lightweight geometric mechanisms that enforce temporal consistency across frames. Two key components enable robust reconstruction: Pose Locking, which anchors pose parameters using stable keyframes, and Rigid Fallback, which recovers meshes under severe occlusion through silhouette-guided rigid alignment. Together, these mechanisms stabilize predictions while remaining compatible with off-the-shelf HMR models. We evaluate Patient4D on 4,680 synthetic surgical sequences and three public HMR video benchmarks. Under surgical drape occlusion, Patient4D achieves a 0.75 mean IoU, reducing failure frames from 30.5% to 1.3% compared to the best baseline. Our findings demonstrate that exploiting stationarity priors can substantially improve monocular reconstruction in clinical AR scenarios.

Hoijoon Jung, Younhyun Jung, Michael Fulham et al.

Mixed reality head-mounted displays (mxdR-HMD) have the potential to visualize volumetric medical imaging data in holograms to provide a true sense of volumetric depth. An effective user interface, however, has yet to be thoroughly studied. Tangible user interfaces (TUIs) enable a tactile interaction with a hologram through an object. The object has physical properties indicating how it might be used with multiple degrees-of-freedom. We propose a TUI using a planar object (PO) for the holographic medical volume visualization and exploration. We refer to it as mxdR hologram slicer (mxdR-HS). Users can slice the hologram to examine particular regions of interest (ROIs) and intermix complementary data and annotations. The mxdR-HS introduces a novel real-time ad-hoc marker-less PO tracking method that works with any PO where corners are visible. The aim of mxdR-HS is to maintain minimum computational latency while preserving practical tracking accuracy to enable seamless TUI integration in the commercial mxdR-HMD, which has limited computational resources. We implemented the mxdR-HS on a commercial Microsoft HoloLens with a built-in depth camera. Our experimental results showed our mxdR-HS had a superior computational latency but marginally lower tracking accuracy than two marker-based tracking methods and resulted in enhanced computational latency and tracking accuracy than 10 marker-less tracking methods. Our mxdR-HS, in a medical environment, can be suggested as a visual guide to display complex volumetric medical imaging data.