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/}

Which Neural Tract Plays a Major Role in Memory Impairment After Multiple Cerebral Infarcts? A Case Report

Injury to the thalamocortical tract (one in the Papez circuit) that leads to memory impairment following brain injury is very rare. In this study, we present a case of partial injury to the thalamocortical tract that causes memory impairment after concurrent thalamic and hippocampal infarct. A 20-year-old male complained of memory impairment 1 month after partial injury to the thalamocortical tract. Using a probabilistic diffusing tensor tractography, it was found that the right thalamocortical tract was thinner than the left thalamocortical tract. However, all other neural tracts including the fornix, cingulum, and mammillothalamic tract were intact on both hemispheres. Therefore, the memory impairment in this patient was considered as being due to thalamic infarct based on the observation that the fornix from hippocampal infarct was intact. This case suggests that the assessment of lesions in the neural tracts of the Papez circuit might be useful for understanding the mechanism of memory impairment following cerebral infarction

Deformable Object Matching Algorithm Using Fast Agglomerative Binary Search Tree Clustering

Deformable objects have changeable shapes and they require a different method of matching algorithm compared to rigid objects. This paper proposes a fast and robust deformable object matching algorithm. First, robust feature points are selected using a statistical characteristic to obtain the feature points with the extraction method. Next, matching pairs are composed by the feature point matching of two images using the matching method. Rapid clustering is performed using the BST (Binary Search Tree) method by obtaining the geometric similarity between the matching pairs. Finally, the matching of the two images is determined after verifying the suitability of the composed cluster. An experiment with five different image sets with deformable objects confirmed the superior robustness and independence of the proposed algorithm while demonstrating up to 60 times faster matching speed compared to the conventional deformable object matching algorithms

Adaptive Image Matching Using Discrimination of Deformable Objects

We propose an efficient image-matching method for deformable-object image matching using discrimination of deformable objects and geometric similarity clustering between feature-matching pairs. A deformable transformation maintains a particular form in the whole image, despite local and irregular deformations. Therefore, the matching information is statistically analyzed to calculate the possibility of deformable transformations, and the images can be identified using the proposed method. In addition, a method for matching deformable object images is proposed, which clusters matching pairs with similar types of geometric deformations. Discrimination of deformable images showed about 90% accuracy, and the proposed deformable image-matching method showed an average 89% success rate and 91% accuracy with various transformations. Therefore, the proposed method robustly matches images, even with various kinds of deformation that can occur in them

Placement Retargeting of Virtual Avatars to Dissimilar Indoor Environments

Rapidly developing technologies are realizing a 3D telepresence, in which geographically separated users can interact with each other through their virtual avatars. In this paper, we present novel methods to determine the avatar's position in an indoor space to preserve the semantics of the user's position in a dissimilar indoor space with different space configurations and furniture layouts. To this end, we first perform a user survey on the preferred avatar placements for various indoor configurations and user placements, and identify a set of related attributes, including interpersonal relation, visual attention, pose, and spatial characteristics, and quantify these attributes with a set of features. By using the obtained dataset and identified features, we train a neural network that predicts the similarity between two placements. Next, we develop an avatar placement method that preserves the semantics of the placement of the remote user in a different space as much as possible. We show the effectiveness of our methods by implementing a prototype AR-based telepresence system and user evaluations.Comment: IEEE Transactions on Visualization and Computer Graphics (Early Access