MOVIN: Real-time Motion Capture using a Single LiDAR

Recent advancements in technology have brought forth new forms of interactive applications, such as the social metaverse, where end users interact with each other through their virtual avatars. In such applications, precise full-body tracking is essential for an immersive experience and a sense of embodiment with the virtual avatar. However, current motion capture systems are not easily accessible to end users due to their high cost, the requirement for special skills to operate them, or the discomfort associated with wearable devices. In this paper, we present MOVIN, the data-driven generative method for real-time motion capture with global tracking, using a single LiDAR sensor. Our autoregressive conditional variational autoencoder (CVAE) model learns the distribution of pose variations conditioned on the given 3D point cloud from LiDAR.As a central factor for high-accuracy motion capture, we propose a novel feature encoder to learn the correlation between the historical 3D point cloud data and global, local pose features, resulting in effective learning of the pose prior. Global pose features include root translation, rotation, and foot contacts, while local features comprise joint positions and rotations. Subsequently, a pose generator takes into account the sampled latent variable along with the features from the previous frame to generate a plausible current pose. Our framework accurately predicts the performer's 3D global information and local joint details while effectively considering temporally coherent movements across frames. We demonstrate the effectiveness of our architecture through quantitative and qualitative evaluations, comparing it against state-of-the-art methods. Additionally, we implement a real-time application to showcase our method in real-world scenarios. MOVIN dataset is available at \url{https://movin3d.github.io/movin_pg2023/}

Fabrication of Gold Nanodot Array for the Localized Surface Plasmon Resonance

Localized surface plasmon resonance (LSPR) is a promising method for detecting antigen-antibody binding in label-free biosensors. In this study, the fabrication of a LSPR substrate with a gold nanodot array through the lift-off process of an alumina mask is reported. The substrate showed an extinction peak in its extinction spectrum, and the peak position was dependent on the height of the gold nanodot array, and the change of extinction peak with the height could be predicted by the numerical simulation. In addition, the peak position was observed to be red-shifted with the increasing RIU value of the medium surrounding the gold nanodot array. In particular, the peak position in the 10 nm thick gold nanodot array was approximately 710 nm in air, and the sensitivity, defined as the ratio of the shift of peak position to the RIU of the medium, was 323.6 nm/RIU. The fabrication procedure could be applied to fabricate the LSPR substrates with a large area